Systems and methods for vehicle navigation

ABSTRACT

Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may be programmed to receive, from a camera, a captured image representative of features in an environment of the vehicle. The processor may generate a warped image based on the received captured image, which may simulate a view of the features in the environment of the vehicle from a simulated viewpoint elevated relative to an actual position of the camera. The processor may further identify a road feature represented in the warped image, which may be transformed in one or more respects relative to a representation of the road feature in the captured image. The processor may then determine a navigational action for the vehicle based on the identified feature represented in the warped image and cause at least one actuator system of the vehicle to implement the determined navigational action.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalApplication No. 62/800,845, filed on Feb. 4, 2019, and U.S. ProvisionalApplication No. 62/956,981, filed on Jan. 3, 2020. The foregoingapplications are incorporated herein by reference in their entirety.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehiclenavigation.

Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Autonomous vehicles may need to take into account a variety of factorsand make appropriate decisions based on those factors to safely andaccurately reach an intended destination. For example, an autonomousvehicle may need to process and interpret visual information (e.g.,information captured from a camera) and may also use informationobtained from other sources (e.g., from a GPS device, a speed sensor, anaccelerometer, a suspension sensor, etc.). At the same time, in order tonavigate to a destination, an autonomous vehicle may also need toidentify its location within a particular roadway (e.g., a specific lanewithin a multi-lane road), navigate alongside other vehicles, avoidobstacles and pedestrians, observe traffic signals and signs, and travelfrom one road to another road at appropriate intersections orinterchanges. Harnessing and interpreting vast volumes of informationcollected by an autonomous vehicle as the vehicle travels to itsdestination poses a multitude of design challenges. The sheer quantityof data (e.g., captured image data, map data, GPS data, sensor data,etc.) that an autonomous vehicle may need to analyze, access, and/orstore poses challenges that can in fact limit or even adversely affectautonomous navigation. Furthermore, if an autonomous vehicle relies ontraditional mapping technology to navigate, the sheer volume of dataneeded to store and update the map poses daunting challenges.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle. The disclosed systems may provide anavigational response based on, for example, an analysis of imagescaptured by one or more of the cameras.

In an embodiment, a navigation system for a vehicle may comprise atleast one processor. The processor may be programmed to receive, from acamera, at least one captured image representative of features in anenvironment of the vehicle; generate a warped image based on thereceived at least one captured image, wherein the warped image simulatesa view of one or more of the features in the environment of the vehiclefrom a simulated viewpoint elevated relative to an actual position ofthe camera; identify at least one road feature represented in the warpedimage, wherein the representation of the at least one road feature inthe warped image is transformed in one or more respects relative to arepresentation of the at least one road feature in the at least onecaptured image; determine a navigational action for the vehicle based onthe identified at least one road feature represented in the warpedimage; and cause at least one actuator system of the vehicle toimplement the determined navigational action.

In an embodiment, a method for navigating a vehicle may comprisereceiving, from a camera, at least one captured image representative offeatures in an environment of the vehicle; generating a warped imagebased on the received at least one captured image, wherein the warpedimage simulates a view of one or more of the features in the environmentof the vehicle from a simulated viewpoint elevated relative to an actualposition of the camera; identifying at least one road featurerepresented in the warped image, wherein the representation of the atleast one road feature in the warped image is transformed in one or morerespects relative to a representation of the at least one road featurein the at least one captured image; determining a navigational actionfor the vehicle based on the identified at least one road featurerepresented in the warped image; and causing at least one actuatorsystem of the vehicle to implement the determined navigational action.

In an embodiment, a system for mapping a road feature for use in vehiclenavigation may comprise at least one processor. The processor may beprogrammed to receive, from a camera, at least one captured imagerepresentative of features in an environment of the vehicle; generate awarped image based on the received at least one captured image, whereinthe warped image simulates a view of one or more of the features in theenvironment of the vehicle from a simulated viewpoint elevated relativeto an actual position of the camera; identify at least one road featurerepresented in the warped image, wherein the representation of the atleast one road feature in the warped image is transformed in one or morerespects relative to a representation of the at least one road featurein the at least one captured image; determine a position of one or morepoints associated with the identified at least one road feature; andforward the determined position to a server for use in updating a roadnavigation model.

In an embodiment, a method for mapping a road feature for use in vehiclenavigation may comprise receiving, from a camera, at least one capturedimage representative of features in an environment of the vehicle;generating a warped image based on the received at least one capturedimage, wherein the warped image simulates a view of one or more of thefeatures in the environment of the vehicle from a simulated viewpointelevated relative to an actual position of the camera; identifying atleast one road feature represented in the warped image, wherein therepresentation of the at least one road feature in the warped image istransformed in one or more respects relative to a representation of theat least one road feature in the at least one captured image;determining a position of one or more points associated with theidentified at least one road feature; and forwarding the determinedposition to a server for use in updating a road navigation model.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 shows a sparse map for providing autonomous vehicle navigation,consistent with the disclosed embodiments.

FIG. 9A illustrates a polynomial representation of a portions of a roadsegment consistent with the disclosed embodiments.

FIG. 9B illustrates a curve in three-dimensional space representing atarget trajectory of a vehicle, for a particular road segment, includedin a sparse map consistent with the disclosed embodiments.

FIG. 10 illustrates example landmarks that may be included in sparse mapconsistent with the disclosed embodiments.

FIG. 11A shows polynomial representations of trajectories consistentwith the disclosed embodiments.

FIGS. 11B and 11C show target trajectories along a multi-lane roadconsistent with disclosed embodiments.

FIG. 11D shows an example road signature profile consistent withdisclosed embodiments.

FIG. 12 is a schematic illustration of a system that uses crowd sourcingdata received from a plurality of vehicles for autonomous vehiclenavigation, consistent with the disclosed embodiments.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines, consistent withthe disclosed embodiments.

FIG. 14 shows a map skeleton generated from combining locationinformation from many drives, consistent with the disclosed embodiments.

FIG. 15 shows an example of a longitudinal alignment of two drives withexample signs as landmarks, consistent with the disclosed embodiments.

FIG. 16 shows an example of a longitudinal alignment of many drives withan example sign as a landmark, consistent with the disclosedembodiments.

FIG. 17 is a schematic illustration of a system for generating drivedata using a camera, a vehicle, and a server, consistent with thedisclosed embodiments.

FIG. 18 is a schematic illustration of a system for crowdsourcing asparse map, consistent with the disclosed embodiments.

FIG. 19 is a flowchart showing an exemplary process for generating asparse map for autonomous vehicle navigation along a road segment,consistent with the disclosed embodiments.

FIG. 20 illustrates a block diagram of a server consistent with thedisclosed embodiments.

FIG. 21 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles, consistent with the disclosed embodiments.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation, consistent with the disclosed embodiments.

FIGS. 24A, 24B, 24C, and 24D illustrate exemplary lane marks that may bedetected consistent with the disclosed embodiments.

FIG. 24E shows exemplary mapped lane marks consistent with the disclosedembodiments.

FIG. 24F shows an exemplary anomaly associated with detecting a lanemark consistent with the disclosed embodiments.

FIG. 25A shows an exemplary image of a vehicle's surrounding environmentfor navigation based on the mapped lane marks consistent with thedisclosed embodiments.

FIG. 25B illustrates a lateral localization correction of a vehiclebased on mapped lane marks in a road navigation model consistent withthe disclosed embodiments.

FIG. 26A is a flowchart showing an exemplary process for mapping a lanemark for use in autonomous vehicle navigation consistent with disclosedembodiments.

FIG. 26B is a flowchart showing an exemplary process for autonomouslynavigating a host vehicle along a road segment using mapped lane marksconsistent with disclosed embodiments.

FIG. 27 is a diagrammatic side view representation of an example vehiclefor use in generating a simulated drone view consistent with disclosedembodiments.

FIGS. 28A and 29A illustrate an example image transformation consistentwith disclosed embodiments.

FIG. 29A is an illustration of an example captured image that may beused to generate a warped image consistent with disclosed embodiments.

FIG. 29B is an illustration of an example warped image that may begenerated based on a captured image consistent with disclosedembodiments.

FIG. 30 is a flowchart showing an example process for mapping a roadfeature for use in vehicle navigation consistent with disclosedembodiments.

FIGS. 31A, 31B, and 31C illustrate an example process for detectingstart and end points of a lane mark consistent with disclosedembodiments.

FIG. 32 is a flowchart showing an example process for mapping a roadfeature for use in vehicle navigation consistent with disclosedembodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration of the vehicle. To beautonomous, a vehicle need not be fully automatic (e.g., fully operationwithout a driver or without driver input). Rather, an autonomous vehicleincludes those that can operate under driver control during certain timeperiods and without driver control during other time periods. Autonomousvehicles may also include vehicles that control only some aspects ofvehicle navigation, such as steering (e.g., to maintain a vehicle coursebetween vehicle lane constraints), but may leave other aspects to thedriver (e.g., braking). In some cases, autonomous vehicles may handlesome or all aspects of braking, speed control, and/or steering of thevehicle.

As human drivers typically rely on visual cues and observations tocontrol a vehicle, transportation infrastructures are built accordingly,with lane markings, traffic signs, and traffic lights are all designedto provide visual information to drivers. In view of these designcharacteristics of transportation infrastructures, an autonomous vehiclemay include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, components of the transportationinfrastructure (e.g., lane markings, traffic signs, traffic lights,etc.) that are observable by drivers and other obstacles (e.g., othervehicles, pedestrians, debris, etc.). Additionally, an autonomousvehicle may also use stored information, such as information thatprovides a model of the vehicle's environment when navigating. Forexample, the vehicle may use GPS data, sensor data (e.g., from anaccelerometer, a speed sensor, a suspension sensor, etc.), and/or othermap data to provide information related to its environment while thevehicle is traveling, and the vehicle (as well as other vehicles) mayuse the information to localize itself on the model.

In some embodiments in this disclosure, an autonomous vehicle may useinformation obtained while navigating (e.g., from a camera, GPS device,an accelerometer, a speed sensor, a suspension sensor, etc.). In otherembodiments, an autonomous vehicle may use information obtained frompast navigations by the vehicle (or by other vehicles) while navigating.In yet other embodiments, an autonomous vehicle may use a combination ofinformation obtained while navigating and information obtained from pastnavigations. The following sections provide an overview of a systemconsistent with the disclosed embodiments, followed by an overview of aforward-facing imaging system and methods consistent with the system.The sections that follow disclose systems and methods for constructing,using, and updating a sparse map for autonomous vehicle navigation.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. In some embodiments,image acquisition unit 120 may include one or more image capture devices(e.g., cameras), such as image capture device 122, image capture device124, and image capture device 126. System 100 may also include a datainterface 128 communicatively connecting processing device 110 to imageacquisition device 120. For example, data interface 128 may include anywired and/or wireless link or links for transmitting image data acquiredby image accusation device 120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).Such transmissions can include communications from the host vehicle toone or more remotely located servers. Such transmissions may alsoinclude communications (one-way or two-way) between the host vehicle andone or more target vehicles in an environment of the host vehicle (e.g.,to facilitate coordination of navigation of the host vehicle in view ofor together with target vehicles in the environment of the hostvehicle), or even a broadcast transmission to unspecified recipients ina vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), agraphics processing unit (GPU), a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices suitable for running applications and for imageprocessing and analysis. In some embodiments, applications processor 180and/or image processor 190 may include any type of single or multi-coreprocessor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example,processors available from manufacturers such as Intel®, AMD®, etc., orGPUs available from manufacturers such as NVIDIA®, ATI®, etc. and mayinclude various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments. In other examples, the EyeQ4® and/or the the EyeQ5® may beused in the disclosed embodiments. Of course, any newer or future EyeQprocessing devices may also be used together with the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. For example, processing devices such as field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),and the like may be configured using, for example, one or more hardwaredescription languages (HDLs).

In other embodiments, configuring a processing device may includestoring executable instructions on a memory that is accessible to theprocessing device during operation. For example, the processing devicemay access the memory to obtain and execute the stored instructionsduring operation. In either case, the processing device configured toperform the sensing, image analysis, and/or navigational functionsdisclosed herein represents a specialized hardware-based system incontrol of multiple hardware based components of a host vehicle.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), a graphicsprocessing unit (GPU), support circuits, digital signal processors,integrated circuits, memory, or any other types of devices for imageprocessing and analysis. The image preprocessor may include a videoprocessor for capturing, digitizing and processing the imagery from theimage sensors. The CPU may comprise any number of microcontrollers ormicroprocessors. The GPU may also comprise any number ofmicrocontrollers or microprocessors. The support circuits may be anynumber of circuits generally well known in the art, including cache,power supply, clock and input-output circuits. The memory may storesoftware that, when executed by the processor, controls the operation ofthe system. The memory may include databases and image processingsoftware. The memory may comprise any number of random access memories,read only memories, flash memories, disk drives, optical storage, tapestorage, removable storage and other types of storage. In one instance,the memory may be separate from the processing unit 110. In anotherinstance, the memory may be integrated into the processing unit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware, as well as a trained system, such as a neural network, or adeep neural network, for example. The memory units may include randomaccess memory (RAM), read only memory (ROM), flash memory, disk drives,optical storage, tape storage, removable storage and/or any other typesof storage. In some embodiments, memory units 140, 150 may be separatefrom the applications processor 180 and/or image processor 190. In otherembodiments, these memory units may be integrated into applicationsprocessor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a tachometer, a speedometer) for measuring a speed ofvehicle 200 and/or an accelerometer (either single axis or multiaxis)for measuring acceleration of vehicle 200.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.). In some cases, map database 160 may store a sparse datamodel including polynomial representations of certain road features(e.g., lane markings) or target trajectories for the host vehicle.Systems and methods of generating such a map are discussed below withreferences to FIGS. 8-19 .

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1 . Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive dataover one or more networks (e.g., cellular networks, the Internet, etc.).For example, wireless transceiver 172 may upload data collected bysystem 100 to one or more servers, and download data from the one ormore servers. Via wireless transceiver 172, system 100 may receive, forexample, periodic or on demand updates to data stored in map database160, memory 140, and/or memory 150. Similarly, wireless transceiver 172may upload any data (e.g., images captured by image acquisition unit120, data received by position sensor 130 or other sensors, vehiclecontrol systems, etc.) from by system 100 and/or any data processed byprocessing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under setting a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead of transmitdata, such as captured images and/or limited location informationrelated to a route.

Other privacy levels are contemplated. For example, system 100 maytransmit data to a server according to an “intermediate” privacy leveland include additional information not included under a “high” privacylevel, such as a make and/or model of a vehicle and/or a vehicle type(e.g., a passenger vehicle, sport utility vehicle, truck, etc.). In someembodiments, system 100 may upload data according to a “low” privacylevel. Under a “low” privacy level setting, system 100 may upload dataand include information sufficient to uniquely identify a specificvehicle, owner/driver, and/or a portion or entirely of a route traveledby the vehicle. Such “low” privacy level data may include one or moreof, for example, a VIN, a driver/owner name, an origination point of avehicle prior to departure, an intended destination of the vehicle, amake and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV. In some embodiments, imagecapture device 122 may be a 7.2M pixel image capture device with anaspect ratio of about 2:1 (e.g., HxV=3800×1900 pixels) with about 100degree horizontal FOV. Such an image capture device may be used in placeof a three image capture device configuration. Due to significant lensdistortion, the vertical FOV of such an image capture device may besignificantly less than 50 degrees in implementations in which the imagecapture device uses a radially symmetric lens. For example, such a lensmay not be radially symmetric which would allow for a vertical FOVgreater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e g,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5Mpixel, 7M pixel, 10M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7 , below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that the shield aligns against a vehicle windshieldhaving a matching slope. In some embodiments, each of image capturedevices 122, 124, and 126 may be positioned behind glare shield 380, asdepicted, for example, in FIG. 3D. The disclosed embodiments are notlimited to any particular configuration of image capture devices 122,124, and 126, camera mount 370, and glare shield 380. FIG. 3C is anillustration of camera mount 370 shown in FIG. 3B from a frontperspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122, 124, and 126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122, 124, and 126 may be positioned behindrearview mirror 310 and positioned substantially side-by-side (e.g., 6cm apart). Further, in some embodiments, as discussed above, one or moreof image capture devices 122, 124, and 126 may be mounted behind glareshield 380 that is flush with the windshield of vehicle 200. Suchshielding may act to minimize the impact of any reflections from insidethe car on image capture devices 122, 124, and 126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122, 124, and 126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system). Furthermore, in some embodiments,redundancy and validation of received data may be supplemented based oninformation received from one more sensors (e.g., radar, lidar, acousticsensors, information received from one or more transceivers outside of avehicle, etc.).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4 , memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402, 404, 406,and 408 included in memory 140. One of skill in the art will understandthat references in the following discussions to processing unit 110 mayrefer to application processor 180 and image processor 190 individuallyor collectively. Accordingly, steps of any of the following processesmay be performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar, lidar, etc.) to perform the monocular image analysis. Asdescribed in connection with FIGS. 5A-5D below, monocular image analysismodule 402 may include instructions for detecting a set of featureswithin the set of images, such as lane markings, vehicles, pedestrians,road signs, highway exit ramps, traffic lights, hazardous objects, andany other feature associated with an environment of a vehicle. Based onthe analysis, system 100 (e.g., via processing unit 110) may cause oneor more navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408. Furthermore, in some embodiments, stereo image analysismodule 404 may implement techniques associated with a trained system(such as a neural network or a deep neural network) or an untrainedsystem, such as a system that may be configured to use computer visionalgorithms to detect and/or label objects in an environment from whichsensory information was captured and processed. In one embodiment,stereo image analysis module 404 and/or other image processing modulesmay be configured to use a combination of a trained and untrainedsystem.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4 . Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550, 552, 554, and 556, processing unit 110 mayidentify road marks appearing within the set of captured images andderive lane geometry information. Based on the identification and thederived information, processing unit 110 may cause one or morenavigational responses in vehicle 200, as described in connection withFIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560, 562, and 564, processing unit 110may identify traffic lights appearing within the set of captured imagesand analyze junction geometry information. Based on the identificationand the analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(i), z_(i))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x₁/z₁). Processingunit 110 may determine the yaw rate command as the product of theheading error and a high-level control gain. The high-level control gainmay be equal to: (2/look-ahead time), if the look-ahead distance is notat the lower bound. Otherwise, the high-level control gain may be equalto: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4 . Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6 , above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6 , above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG. 4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Sparse Road Model for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may use a sparsemap for autonomous vehicle navigation. In particular, the sparse map maybe for autonomous vehicle navigation along a road segment. For example,the sparse map may provide sufficient information for navigating anautonomous vehicle without storing and/or updating a large quantity ofdata. As discussed below in further detail, an autonomous vehicle mayuse the sparse map to navigate one or more roads based on one or morestored trajectories.

Sparse Map for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may generate asparse map for autonomous vehicle navigation. For example, the sparsemap may provide sufficient information for navigation without requiringexcessive data storage or data transfer rates. As discussed below infurther detail, a vehicle (which may be an autonomous vehicle) may usethe sparse map to navigate one or more roads. For example, in someembodiments, the sparse map may include data related to a road andpotentially landmarks along the road that may be sufficient for vehiclenavigation, but which also exhibit small data footprints. For example,the sparse data maps described in detail below may require significantlyless storage space and data transfer bandwidth as compared with digitalmaps including detailed map information, such as image data collectedalong a road.

For example, rather than storing detailed representations of a roadsegment, the sparse data map may store three-dimensional polynomialrepresentations of preferred vehicle paths along a road. These paths mayrequire very little data storage space. Further, in the described sparsedata maps, landmarks may be identified and included in the sparse maproad model to aid in navigation. These landmarks may be located at anyspacing suitable for enabling vehicle navigation, but in some cases,such landmarks need not be identified and included in the model at highdensities and short spacings. Rather, in some cases, navigation may bepossible based on landmarks that are spaced apart by at least 50 meters,at least 100 meters, at least 500 meters, at least 1 kilometer, or atleast 2 kilometers. As will be discussed in more detail in othersections, the sparse map may be generated based on data collected ormeasured by vehicles equipped with various sensors and devices, such asimage capture devices, Global Positioning System sensors, motionsensors, etc., as the vehicles travel along roadways. In some cases, thesparse map may be generated based on data collected during multipledrives of one or more vehicles along a particular roadway. Generating asparse map using multiple drives of one or more vehicles may be referredto as “crowdsourcing” a sparse map.

Consistent with disclosed embodiments, an autonomous vehicle system mayuse a sparse map for navigation. For example, the disclosed systems andmethods may distribute a sparse map for generating a road navigationmodel for an autonomous vehicle and may navigate an autonomous vehiclealong a road segment using a sparse map and/or a generated roadnavigation model. Sparse maps consistent with the present disclosure mayinclude one or more three-dimensional contours that may representpredetermined trajectories that autonomous vehicles may traverse as theymove along associated road segments.

Sparse maps consistent with the present disclosure may also include datarepresenting one or more road features. Such road features may includerecognized landmarks, road signature profiles, and any otherroad-related features useful in navigating a vehicle. Sparse mapsconsistent with the present disclosure may enable autonomous navigationof a vehicle based on relatively small amounts of data included in thesparse map. For example, rather than including detailed representationsof a road, such as road edges, road curvature, images associated withroad segments, or data detailing other physical features associated witha road segment, the disclosed embodiments of the sparse map may requirerelatively little storage space (and relatively little bandwidth whenportions of the sparse map are transferred to a vehicle) but may stilladequately provide for autonomous vehicle navigation. The small datafootprint of the disclosed sparse maps, discussed in further detailbelow, may be achieved in some embodiments by storing representations ofroad-related elements that require small amounts of data but stillenable autonomous navigation.

For example, rather than storing detailed representations of variousaspects of a road, the disclosed sparse maps may store polynomialrepresentations of one or more trajectories that a vehicle may followalong the road. Thus, rather than storing (or having to transfer)details regarding the physical nature of the road to enable navigationalong the road, using the disclosed sparse maps, a vehicle may benavigated along a particular road segment without, in some cases, havingto interpret physical aspects of the road, but rather, by aligning itspath of travel with a trajectory (e.g., a polynomial spline) along theparticular road segment. In this way, the vehicle may be navigated basedmainly upon the stored trajectory (e.g., a polynomial spline) that mayrequire much less storage space than an approach involving storage ofroadway images, road parameters, road layout, etc.

In addition to the stored polynomial representations of trajectoriesalong a road segment, the disclosed sparse maps may also include smalldata objects that may represent a road feature. In some embodiments, thesmall data objects may include digital signatures, which are derivedfrom a digital image (or a digital signal) that was obtained by a sensor(e.g., a camera or other sensor, such as a suspension sensor) onboard avehicle traveling along the road segment. The digital signature may havea reduced size relative to the signal that was acquired by the sensor.In some embodiments, the digital signature may be created to becompatible with a classifier function that is configured to detect andto identify the road feature from the signal that is acquired by thesensor, for example, during a subsequent drive. In some embodiments, adigital signature may be created such that the digital signature has afootprint that is as small as possible, while retaining the ability tocorrelate or match the road feature with the stored signature based onan image (or a digital signal generated by a sensor, if the storedsignature is not based on an image and/or includes other data) of theroad feature that is captured by a camera onboard a vehicle travelingalong the same road segment at a subsequent time.

In some embodiments, a size of the data objects may be furtherassociated with a uniqueness of the road feature. For example, for aroad feature that is detectable by a camera onboard a vehicle, and wherethe camera system onboard the vehicle is coupled to a classifier that iscapable of distinguishing the image data corresponding to that roadfeature as being associated with a particular type of road feature, forexample, a road sign, and where such a road sign is locally unique inthat area (e.g., there is no identical road sign or road sign of thesame type nearby), it may be sufficient to store data indicating thetype of the road feature and its location.

As will be discussed in further detail below, road features (e.g.,landmarks along a road segment) may be stored as small data objects thatmay represent a road feature in relatively few bytes, while at the sametime providing sufficient information for recognizing and using such afeature for navigation. In one example, a road sign may be identified asa recognized landmark on which navigation of a vehicle may be based. Arepresentation of the road sign may be stored in the sparse map toinclude, e.g., a few bytes of data indicating a type of landmark (e.g.,a stop sign) and a few bytes of data indicating a location of thelandmark (e.g., coordinates). Navigating based on such data-lightrepresentations of the landmarks (e.g., using representations sufficientfor locating, recognizing, and navigating based upon the landmarks) mayprovide a desired level of navigational functionality associated withsparse maps without significantly increasing the data overheadassociated with the sparse maps. This lean representation of landmarks(and other road features) may take advantage of the sensors andprocessors included onboard such vehicles that are configured to detect,identify, and/or classify certain road features.

When, for example, a sign or even a particular type of a sign is locallyunique (e.g., when there is no other sign or no other sign of the sametype) in a given area, the sparse map may use data indicating a type ofa landmark (a sign or a specific type of sign), and during navigation(e.g., autonomous navigation) when a camera onboard an autonomousvehicle captures an image of the area including a sign (or of a specifictype of sign), the processor may process the image, detect the sign (ifindeed present in the image), classify the image as a sign (or as aspecific type of sign), and correlate the location of the image with thelocation of the sign as stored in the sparse map.

Generating a Sparse Map

In some embodiments, a sparse map may include at least one linerepresentation of a road surface feature extending along a road segmentand a plurality of landmarks associated with the road segment. Incertain aspects, the sparse map may be generated via “crowdsourcing,”for example, through image analysis of a plurality of images acquired asone or more vehicles traverse the road segment.

FIG. 8 shows a sparse map 800 that one or more vehicles, e.g., vehicle200 (which may be an autonomous vehicle), may access for providingautonomous vehicle navigation. Sparse map 800 may be stored in a memory,such as memory 140 or 150. Such memory devices may include any types ofnon-transitory storage devices or computer-readable media. For example,in some embodiments, memory 140 or 150 may include hard drives, compactdiscs, flash memory, magnetic based memory devices, optical based memorydevices, etc. In some embodiments, sparse map 800 may be stored in adatabase (e.g., map database 160) that may be stored in memory 140 or150, or other types of storage devices.

In some embodiments, sparse map 800 may be stored on a storage device ora non-transitory computer-readable medium provided onboard vehicle 200(e.g., a storage device included in a navigation system onboard vehicle200). A processor (e.g., processing unit 110) provided on vehicle 200may access sparse map 800 stored in the storage device orcomputer-readable medium provided onboard vehicle 200 in order togenerate navigational instructions for guiding the autonomous vehicle200 as the vehicle traverses a road segment.

Sparse map 800 need not be stored locally with respect to a vehicle,however. In some embodiments, sparse map 800 may be stored on a storagedevice or computer-readable medium provided on a remote server thatcommunicates with vehicle 200 or a device associated with vehicle 200. Aprocessor (e.g., processing unit 110) provided on vehicle 200 mayreceive data included in sparse map 800 from the remote server and mayexecute the data for guiding the autonomous driving of vehicle 200. Insuch embodiments, the remote server may store all of sparse map 800 oronly a portion thereof. Accordingly, the storage device orcomputer-readable medium provided onboard vehicle 200 and/or onboard oneor more additional vehicles may store the remaining portion(s) of sparsemap 800.

Furthermore, in such embodiments, sparse map 800 may be made accessibleto a plurality of vehicles traversing various road segments (e.g., tens,hundreds, thousands, or millions of vehicles, etc.). It should be notedalso that sparse map 800 may include multiple sub-maps. For example, insome embodiments, sparse map 800 may include hundreds, thousands,millions, or more, of sub-maps that may be used in navigating a vehicle.Such sub-maps may be referred to as local maps, and a vehicle travelingalong a roadway may access any number of local maps relevant to alocation in which the vehicle is traveling. The local map sections ofsparse map 800 may be stored with a Global Navigation Satellite System(GNSS) key as an index to the database of sparse map 800. Thus, whilecomputation of steering angles for navigating a host vehicle in thepresent system may be performed without reliance upon a GNSS position ofthe host vehicle, road features, or landmarks, such GNSS information maybe used for retrieval of relevant local maps.

In general, sparse map 800 may be generated based on data collected fromone or more vehicles as they travel along roadways. For example, usingsensors aboard the one or more vehicles (e.g., cameras, speedometers,GPS, accelerometers, etc.), the trajectories that the one or morevehicles travel along a roadway may be recorded, and the polynomialrepresentation of a preferred trajectory for vehicles making subsequenttrips along the roadway may be determined based on the collectedtrajectories travelled by the one or more vehicles. Similarly, datacollected by the one or more vehicles may aid in identifying potentiallandmarks along a particular roadway. Data collected from traversingvehicles may also be used to identify road profile information, such asroad width profiles, road roughness profiles, traffic line spacingprofiles, road conditions, etc. Using the collected information, sparsemap 800 may be generated and distributed (e.g., for local storage or viaon-the-fly data transmission) for use in navigating one or moreautonomous vehicles. However, in some embodiments, map generation maynot end upon initial generation of the map. As will be discussed ingreater detail below, sparse map 800 may be continuously or periodicallyupdated based on data collected from vehicles as those vehicles continueto traverse roadways included in sparse map 800.

Data recorded in sparse map 800 may include position information basedon Global Positioning System (GPS) data. For example, locationinformation may be included in sparse map 800 for various map elements,including, for example, landmark locations, road profile locations, etc.Locations for map elements included in sparse map 800 may be obtainedusing GPS data collected from vehicles traversing a roadway. Forexample, a vehicle passing an identified landmark may determine alocation of the identified landmark using GPS position informationassociated with the vehicle and a determination of a location of theidentified landmark relative to the vehicle (e.g., based on imageanalysis of data collected from one or more cameras on board thevehicle). Such location determinations of an identified landmark (or anyother feature included in sparse map 800) may be repeated as additionalvehicles pass the location of the identified landmark. Some or all ofthe additional location determinations may be used to refine thelocation information stored in sparse map 800 relative to the identifiedlandmark. For example, in some embodiments, multiple positionmeasurements relative to a particular feature stored in sparse map 800may be averaged together. Any other mathematical operations, however,may also be used to refine a stored location of a map element based on aplurality of determined locations for the map element.

The sparse map of the disclosed embodiments may enable autonomousnavigation of a vehicle using relatively small amounts of stored data.In some embodiments, sparse map 800 may have a data density (e.g.,including data representing the target trajectories, landmarks, and anyother stored road features) of less than 2 MB per kilometer of roads,less than 1 MB per kilometer of roads, less than 500 kB per kilometer ofroads, or less than 100 kB per kilometer of roads. In some embodiments,the data density of sparse map 800 may be less than 10 kB per kilometerof roads or even less than 2 kB per kilometer of roads (e.g., 1.6 kB perkilometer), or no more than 10 kB per kilometer of roads, or no morethan 20 kB per kilometer of roads. In some embodiments, most, if notall, of the roadways of the United States may be navigated autonomouslyusing a sparse map having a total of 4 GB or less of data. These datadensity values may represent an average over an entire sparse map 800,over a local map within sparse map 800, and/or over a particular roadsegment within sparse map 800.

As noted, sparse map 800 may include representations of a plurality oftarget trajectories 810 for guiding autonomous driving or navigationalong a road segment. Such target trajectories may be stored asthree-dimensional splines. The target trajectories stored in sparse map800 may be determined based on two or more reconstructed trajectories ofprior traversals of vehicles along a particular road segment, forexample. A road segment may be associated with a single targettrajectory or multiple target trajectories. For example, on a two laneroad, a first target trajectory may be stored to represent an intendedpath of travel along the road in a first direction, and a second targettrajectory may be stored to represent an intended path of travel alongthe road in another direction (e.g., opposite to the first direction).Additional target trajectories may be stored with respect to aparticular road segment. For example, on a multi-lane road one or moretarget trajectories may be stored representing intended paths of travelfor vehicles in one or more lanes associated with the multi-lane road.In some embodiments, each lane of a multi-lane road may be associatedwith its own target trajectory. In other embodiments, there may be fewertarget trajectories stored than lanes present on a multi-lane road. Insuch cases, a vehicle navigating the multi-lane road may use any of thestored target trajectories to guides its navigation by taking intoaccount an amount of lane offset from a lane for which a targettrajectory is stored (e.g., if a vehicle is traveling in the left mostlane of a three lane highway, and a target trajectory is stored only forthe middle lane of the highway, the vehicle may navigate using thetarget trajectory of the middle lane by accounting for the amount oflane offset between the middle lane and the left-most lane whengenerating navigational instructions).

In some embodiments, the target trajectory may represent an ideal paththat a vehicle should take as the vehicle travels. The target trajectorymay be located, for example, at an approximate center of a lane oftravel. In other cases, the target trajectory may be located elsewhererelative to a road segment. For example, a target trajectory mayapproximately coincide with a center of a road, an edge of a road, or anedge of a lane, etc. In such cases, navigation based on the targettrajectory may include a determined amount of offset to be maintainedrelative to the location of the target trajectory. Moreover, in someembodiments, the determined amount of offset to be maintained relativeto the location of the target trajectory may differ based on a type ofvehicle (e.g., a passenger vehicle including two axles may have adifferent offset from a truck including more than two axles along atleast a portion of the target trajectory).

Sparse map 800 may also include data relating to a plurality ofpredetermined landmarks 820 associated with particular road segments,local maps, etc. As discussed in greater detail below, these landmarksmay be used in navigation of the autonomous vehicle. For example, insome embodiments, the landmarks may be used to determine a currentposition of the vehicle relative to a stored target trajectory. Withthis position information, the autonomous vehicle may be able to adjusta heading direction to match a direction of the target trajectory at thedetermined location.

The plurality of landmarks 820 may be identified and stored in sparsemap 800 at any suitable spacing. In some embodiments, landmarks may bestored at relatively high densities (e.g., every few meters or more). Insome embodiments, however, significantly larger landmark spacing valuesmay be employed. For example, in sparse map 800, identified (orrecognized) landmarks may be spaced apart by 10 meters, 20 meters, 50meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, theidentified landmarks may be located at distances of even more than 2kilometers apart.

Between landmarks, and therefore between determinations of vehicleposition relative to a target trajectory, the vehicle may navigate basedon dead reckoning in which the vehicle uses sensors to determine its egomotion and estimate its position relative to the target trajectory.Because errors may accumulate during navigation by dead reckoning, overtime the position determinations relative to the target trajectory maybecome increasingly less accurate. The vehicle may use landmarksoccurring in sparse map 800 (and their known locations) to remove thedead reckoning-induced errors in position determination. In this way,the identified landmarks included in sparse map 800 may serve asnavigational anchors from which an accurate position of the vehiclerelative to a target trajectory may be determined. Because a certainamount of error may be acceptable in position location, an identifiedlandmark need not always be available to an autonomous vehicle. Rather,suitable navigation may be possible even based on landmark spacings, asnoted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters,1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1identified landmark every 1 km of road may be sufficient to maintain alongitudinal position determination accuracy within 1 m. Thus, not everypotential landmark appearing along a road segment need be stored insparse map 800.

Moreover, in some embodiments, lane markings may be used forlocalization of the vehicle during landmark spacings. By using lanemarkings during landmark spacings, the accumulation of during navigationby dead reckoning may be minimized.

In addition to target trajectories and identified landmarks, sparse map800 may include information relating to various other road features. Forexample, FIG. 9A illustrates a representation of curves along aparticular road segment that may be stored in sparse map 800. In someembodiments, a single lane of a road may be modeled by athree-dimensional polynomial description of left and right sides of theroad. Such polynomials representing left and right sides of a singlelane are shown in FIG. 9A. Regardless of how many lanes a road may have,the road may be represented using polynomials in a way similar to thatillustrated in FIG. 9A. For example, left and right sides of amulti-lane road may be represented by polynomials similar to those shownin FIG. 9A, and intermediate lane markings included on a multi-lane road(e.g., dashed markings representing lane boundaries, solid yellow linesrepresenting boundaries between lanes traveling in different directions,etc.) may also be represented using polynomials such as those shown inFIG. 9A.

As shown in FIG. 9A, a lane 900 may be represented using polynomials(e.g., a first order, second order, third order, or any suitable orderpolynomials). For illustration, lane 900 is shown as a two-dimensionallane and the polynomials are shown as two-dimensional polynomials. Asdepicted in FIG. 9A, lane 900 includes a left side 910 and a right side920. In some embodiments, more than one polynomial may be used torepresent a location of each side of the road or lane boundary. Forexample, each of left side 910 and right side 920 may be represented bya plurality of polynomials of any suitable length. In some cases, thepolynomials may have a length of about 100 m, although other lengthsgreater than or less than 100 m may also be used. Additionally, thepolynomials can overlap with one another in order to facilitate seamlesstransitions in navigating based on subsequently encountered polynomialsas a host vehicle travels along a roadway. For example, each of leftside 910 and right side 920 may be represented by a plurality of thirdorder polynomials separated into segments of about 100 meters in length(an example of the first predetermined range), and overlapping eachother by about 50 meters. The polynomials representing the left side 910and the right side 920 may or may not have the same order. For example,in some embodiments, some polynomials may be second order polynomials,some may be third order polynomials, and some may be fourth orderpolynomials.

In the example shown in FIG. 9A, left side 910 of lane 900 isrepresented by two groups of third order polynomials. The first groupincludes polynomial segments 911, 912, and 913. The second groupincludes polynomial segments 914, 915, and 916. The two groups, whilesubstantially parallel to each other, follow the locations of theirrespective sides of the road. Polynomial segments 911, 912, 913, 914,915, and 916 have a length of about 100 meters and overlap adjacentsegments in the series by about 50 meters. As noted previously, however,polynomials of different lengths and different overlap amounts may alsobe used. For example, the polynomials may have lengths of 500 m, 1 km,or more, and the overlap amount may vary from 0 to 50 m, 50 m to 100 m,or greater than 100 m. Additionally, while FIG. 9A is shown asrepresenting polynomials extending in 2D space (e.g., on the surface ofthe paper), it is to be understood that these polynomials may representcurves extending in three dimensions (e.g., including a heightcomponent) to represent elevation changes in a road segment in additionto X-Y curvature. In the example shown in FIG. 9A, right side 920 oflane 900 is further represented by a first group having polynomialsegments 921, 922, and 923 and a second group having polynomial segments924, 925, and 926.

Returning to the target trajectories of sparse map 800, FIG. 9B shows athree-dimensional polynomial representing a target trajectory for avehicle traveling along a particular road segment. The target trajectoryrepresents not only the X-Y path that a host vehicle should travel alonga particular road segment, but also the elevation change that the hostvehicle will experience when traveling along the road segment. Thus,each target trajectory in sparse map 800 may be represented by one ormore three-dimensional polynomials, like the three-dimensionalpolynomial 950 shown in FIG. 9B. Sparse map 800 may include a pluralityof trajectories (e g, millions or billions or more to representtrajectories of vehicles along various road segments along roadwaysthroughout the world). In some embodiments, each target trajectory maycorrespond to a spline connecting three-dimensional polynomial segments.

Regarding the data footprint of polynomial curves stored in sparse map800, in some embodiments, each third degree polynomial may berepresented by four parameters, each requiring four bytes of data.Suitable representations may be obtained with third degree polynomialsrequiring about 192 bytes of data for every 100 m. This may translate toapproximately 200 kB per hour in data usage/transfer requirements for ahost vehicle traveling approximately 100 km/hr.

Sparse map 800 may describe the lanes network using a combination ofgeometry descriptors and meta-data. The geometry may be described bypolynomials or splines as described above. The meta-data may describethe number of lanes, special characteristics (such as a car pool lane),and possibly other sparse labels. The total footprint of such indicatorsmay be negligible.

Accordingly, a sparse map according to embodiments of the presentdisclosure may include at least one line representation of a roadsurface feature extending along the road segment, each linerepresentation representing a path along the road segment substantiallycorresponding with the road surface feature. In some embodiments, asdiscussed above, the at least one line representation of the roadsurface feature may include a spline, a polynomial representation, or acurve. Furthermore, in some embodiments, the road surface feature mayinclude at least one of a road edge or a lane marking. Moreover, asdiscussed below with respect to “crowdsourcing,” the road surfacefeature may be identified through image analysis of a plurality ofimages acquired as one or more vehicles traverse the road segment.

As previously noted, sparse map 800 may include a plurality ofpredetermined landmarks associated with a road segment. Rather thanstoring actual images of the landmarks and relying, for example, onimage recognition analysis based on captured images and stored images,each landmark in sparse map 800 may be represented and recognized usingless data than a stored, actual image would require. Data representinglandmarks may still include sufficient information for describing oridentifying the landmarks along a road. Storing data describingcharacteristics of landmarks, rather than the actual images oflandmarks, may reduce the size of sparse map 800.

FIG. 10 illustrates examples of types of landmarks that may berepresented in sparse map 800. The landmarks may include any visible andidentifiable objects along a road segment. The landmarks may be selectedsuch that they are fixed and do not change often with respect to theirlocations and/or content. The landmarks included in sparse map 800 maybe useful in determining a location of vehicle 200 with respect to atarget trajectory as the vehicle traverses a particular road segment.Examples of landmarks may include traffic signs, directional signs,general signs (e.g., rectangular signs), roadside fixtures (e.g.,lampposts, reflectors, etc.), and any other suitable category. In someembodiments, lane marks on the road, may also be included as landmarksin sparse map 800.

Examples of landmarks shown in FIG. 10 include traffic signs,directional signs, roadside fixtures, and general signs. Traffic signsmay include, for example, speed limit signs (e.g., speed limit sign1000), yield signs (e.g., yield sign 1005), route number signs (e.g.,route number sign 1010), traffic light signs (e.g., traffic light sign1015), stop signs (e.g., stop sign 1020). Directional signs may includea sign that includes one or more arrows indicating one or moredirections to different places. For example, directional signs mayinclude a highway sign 1025 having arrows for directing vehicles todifferent roads or places, an exit sign 1030 having an arrow directingvehicles off a road, etc. Accordingly, at least one of the plurality oflandmarks may include a road sign.

General signs may be unrelated to traffic. For example, general signsmay include billboards used for advertisement, or a welcome boardadjacent a border between two countries, states, counties, cities, ortowns. FIG. 10 shows a general sign 1040 (“Joe's Restaurant”). Althoughgeneral sign 1040 may have a rectangular shape, as shown in FIG. 10 ,general sign 1040 may have other shapes, such as square, circle,triangle, etc.

Landmarks may also include roadside fixtures. Roadside fixtures may beobjects that are not signs, and may not be related to traffic ordirections. For example, roadside fixtures may include lampposts (e.g.,lamppost 1035), power line posts, traffic light posts, etc.

Landmarks may also include beacons that may be specifically designed forusage in an autonomous vehicle navigation system. For example, suchbeacons may include stand-alone structures placed at predeterminedintervals to aid in navigating a host vehicle. Such beacons may alsoinclude visual/graphical information added to existing road signs (e.g.,icons, emblems, bar codes, etc.) that may be identified or recognized bya vehicle traveling along a road segment. Such beacons may also includeelectronic components. In such embodiments, electronic beacons (e.g.,RFID tags, etc.) may be used to transmit non-visual information to ahost vehicle. Such information may include, for example, landmarkidentification and/or landmark location information that a host vehiclemay use in determining its position along a target trajectory.

In some embodiments, the landmarks included in sparse map 800 may berepresented by a data object of a predetermined size. The datarepresenting a landmark may include any suitable parameters foridentifying a particular landmark. For example, in some embodiments,landmarks stored in sparse map 800 may include parameters such as aphysical size of the landmark (e.g., to support estimation of distanceto the landmark based on a known size/scale), a distance to a previouslandmark, lateral offset, height, a type code (e.g., a landmarktype—what type of directional sign, traffic sign, etc.), a GPScoordinate (e.g., to support global localization), and any othersuitable parameters. Each parameter may be associated with a data size.For example, a landmark size may be stored using 8 bytes of data. Adistance to a previous landmark, a lateral offset, and height may bespecified using 12 bytes of data. A type code associated with a landmarksuch as a directional sign or a traffic sign may require about 2 bytesof data. For general signs, an image signature enabling identificationof the general sign may be stored using 50 bytes of data storage. Thelandmark GPS position may be associated with 16 bytes of data storage.These data sizes for each parameter are examples only, and other datasizes may also be used.

Representing landmarks in sparse map 800 in this manner may offer a leansolution for efficiently representing landmarks in the database. In someembodiments, signs may be referred to as semantic signs and non-semanticsigns. A semantic sign may include any class of signs for which there'sa standardized meaning (e.g., speed limit signs, warning signs,directional signs, etc.). A non-semantic sign may include any sign thatis not associated with a standardized meaning (e.g., general advertisingsigns, signs identifying business establishments, etc.). For example,each semantic sign may be represented with 38 bytes of data (e.g., 8bytes for size; 12 bytes for distance to previous landmark, lateraloffset, and height; 2 bytes for a type code; and 16 bytes for GPScoordinates). Sparse map 800 may use a tag system to represent landmarktypes. In some cases, each traffic sign or directional sign may beassociated with its own tag, which may be stored in the database as partof the landmark identification. For example, the database may include onthe order of 1000 different tags to represent various traffic signs andon the order of about 10000 different tags to represent directionalsigns. Of course, any suitable number of tags may be used, andadditional tags may be created as needed. General purpose signs may berepresented in some embodiments using less than about 100 bytes (e.g.,about 86 bytes including 8 bytes for size; 12 bytes for distance toprevious landmark, lateral offset, and height; 50 bytes for an imagesignature; and 16 bytes for GPS coordinates).

Thus, for semantic road signs not requiring an image signature, the datadensity impact to sparse map 800, even at relatively high landmarkdensities of about 1 per 50 m, may be on the order of about 760 bytesper kilometer (e.g., 20 landmarks per km×38 bytes per landmark=760bytes). Even for general purpose signs including an image signaturecomponent, the data density impact is about 1.72 kB per km (e.g., 20landmarks per km×86 bytes per landmark=1,720 bytes). For semantic roadsigns, this equates to about 76 kB per hour of data usage for a vehicletraveling 100 km/hr. For general purpose signs, this equates to about170 kB per hour for a vehicle traveling 100 km/hr.

In some embodiments, a generally rectangular object, such as arectangular sign, may be represented in sparse map 800 by no more than100 bytes of data. The representation of the generally rectangularobject (e.g., general sign 1040) in sparse map 800 may include acondensed image signature (e.g., condensed image signature 1045)associated with the generally rectangular object. This condensed imagesignature may be used, for example, to aid in identification of ageneral purpose sign, for example, as a recognized landmark. Such acondensed image signature (e.g., image information derived from actualimage data representing an object) may avoid a need for storage of anactual image of an object or a need for comparative image analysisperformed on actual images in order to recognize landmarks.

Referring to FIG. 10 , sparse map 800 may include or store a condensedimage signature 1045 associated with a general sign 1040, rather than anactual image of general sign 1040. For example, after an image capturedevice (e.g., image capture device 122, 124, or 126) captures an imageof general sign 1040, a processor (e.g., image processor 190 or anyother processor that can process images either aboard or remotelylocated relative to a host vehicle) may perform an image analysis toextract/create condensed image signature 1045 that includes a uniquesignature or pattern associated with general sign 1040. In oneembodiment, condensed image signature 1045 may include a shape, colorpattern, a brightness pattern, or any other feature that may beextracted from the image of general sign 1040 for describing generalsign 1040.

For example, in FIG. 10 , the circles, triangles, and stars shown incondensed image signature 1045 may represent areas of different colors.The pattern represented by the circles, triangles, and stars may bestored in sparse map 800, e.g., within the 50 bytes designated toinclude an image signature. Notably, the circles, triangles, and starsare not necessarily meant to indicate that such shapes are stored aspart of the image signature. Rather, these shapes are meant toconceptually represent recognizable areas having discernible colordifferences, textual areas, graphical shapes, or other variations incharacteristics that may be associated with a general purpose sign. Suchcondensed image signatures can be used to identify a landmark in theform of a general sign. For example, the condensed image signature canbe used to perform a same-not-same analysis based on a comparison of astored condensed image signature with image data captured, for example,using a camera onboard an autonomous vehicle.

Accordingly, the plurality of landmarks may be identified through imageanalysis of the plurality of images acquired as one or more vehiclestraverse the road segment. As explained below with respect to“crowdsourcing,” in some embodiments, the image analysis to identify theplurality of landmarks may include accepting potential landmarks when aratio of images in which the landmark does appear to images in which thelandmark does not appear exceeds a threshold. Furthermore, in someembodiments, the image analysis to identify the plurality of landmarksmay include rejecting potential landmarks when a ratio of images inwhich the landmark does not appear to images in which the landmark doesappear exceeds a threshold.

Returning to the target trajectories a host vehicle may use to navigatea particular road segment, FIG. 11A shows polynomial representationstrajectories capturing during a process of building or maintainingsparse map 800. A polynomial representation of a target trajectoryincluded in sparse map 800 may be determined based on two or morereconstructed trajectories of prior traversals of vehicles along thesame road segment. In some embodiments, the polynomial representation ofthe target trajectory included in sparse map 800 may be an aggregationof two or more reconstructed trajectories of prior traversals ofvehicles along the same road segment. In some embodiments, thepolynomial representation of the target trajectory included in sparsemap 800 may be an average of the two or more reconstructed trajectoriesof prior traversals of vehicles along the same road segment. Othermathematical operations may also be used to construct a targettrajectory along a road path based on reconstructed trajectoriescollected from vehicles traversing along a road segment.

As shown in FIG. 11A, a road segment 1100 may be travelled by a numberof vehicles 200 at different times. Each vehicle 200 may collect datarelating to a path that the vehicle took along the road segment. Thepath traveled by a particular vehicle may be determined based on cameradata, accelerometer information, speed sensor information, and/or GPSinformation, among other potential sources. Such data may be used toreconstruct trajectories of vehicles traveling along the road segment,and based on these reconstructed trajectories, a target trajectory (ormultiple target trajectories) may be determined for the particular roadsegment. Such target trajectories may represent a preferred path of ahost vehicle (e.g., guided by an autonomous navigation system) as thevehicle travels along the road segment.

In the example shown in FIG. 11A, a first reconstructed trajectory 1101may be determined based on data received from a first vehicle traversingroad segment 1100 at a first time period (e.g., day 1), a secondreconstructed trajectory 1102 may be obtained from a second vehicletraversing road segment 1100 at a second time period (e.g., day 2), anda third reconstructed trajectory 1103 may be obtained from a thirdvehicle traversing road segment 1100 at a third time period (e.g., day3). Each trajectory 1101, 1102, and 1103 may be represented by apolynomial, such as a three-dimensional polynomial. It should be notedthat in some embodiments, any of the reconstructed trajectories may beassembled onboard the vehicles traversing road segment 1100.

Additionally, or alternatively, such reconstructed trajectories may bedetermined on a server side based on information received from vehiclestraversing road segment 1100. For example, in some embodiments, vehicles200 may transmit data to one or more servers relating to their motionalong road segment 1100 (e.g., steering angle, heading, time, position,speed, sensed road geometry, and/or sensed landmarks, among things). Theserver may reconstruct trajectories for vehicles 200 based on thereceived data. The server may also generate a target trajectory forguiding navigation of autonomous vehicle that will travel along the sameroad segment 1100 at a later time based on the first, second, and thirdtrajectories 1101, 1102, and 1103. While a target trajectory may beassociated with a single prior traversal of a road segment, in someembodiments, each target trajectory included in sparse map 800 may bedetermined based on two or more reconstructed trajectories of vehiclestraversing the same road segment. In FIG. 11A, the target trajectory isrepresented by 1110. In some embodiments, the target trajectory 1110 maybe generated based on an average of the first, second, and thirdtrajectories 1101, 1102, and 1103. In some embodiments, the targettrajectory 1110 included in sparse map 800 may be an aggregation (e.g.,a weighted combination) of two or more reconstructed trajectories.

FIGS. 11B and 11C further illustrate the concept of target trajectoriesassociated with road segments present within a geographic region 1111.As shown in FIG. 11B, a first road segment 1120 within geographic region1111 may include a multilane road, which includes two lanes 1122designated for vehicle travel in a first direction and two additionallanes 1124 designated for vehicle travel in a second direction oppositeto the first direction. Lanes 1122 and lanes 1124 may be separated by adouble yellow line 1123. Geographic region 1111 may also include abranching road segment 1130 that intersects with road segment 1120. Roadsegment 1130 may include a two-lane road, each lane being designated fora different direction of travel. Geographic region 1111 may also includeother road features, such as a stop line 1132, a stop sign 1134, a speedlimit sign 1136, and a hazard sign 1138.

As shown in FIG. 11C, sparse map 800 may include a local map 1140including a road model for assisting with autonomous navigation ofvehicles within geographic region 1111. For example, local map 1140 mayinclude target trajectories for one or more lanes associated with roadsegments 1120 and/or 1130 within geographic region 1111. For example,local map 1140 may include target trajectories 1141 and/or 1142 that anautonomous vehicle may access or rely upon when traversing lanes 1122.Similarly, local map 1140 may include target trajectories 1143 and/or1144 that an autonomous vehicle may access or rely upon when traversinglanes 1124. Further, local map 1140 may include target trajectories 1145and/or 1146 that an autonomous vehicle may access or rely upon whentraversing road segment 1130. Target trajectory 1147 represents apreferred path an autonomous vehicle should follow when transitioningfrom lanes 1120 (and specifically, relative to target trajectory 1141associated with a right-most lane of lanes 1120) to road segment 1130(and specifically, relative to a target trajectory 1145 associated witha first side of road segment 1130. Similarly, target trajectory 1148represents a preferred path an autonomous vehicle should follow whentransitioning from road segment 1130 (and specifically, relative totarget trajectory 1146) to a portion of road segment 1124 (andspecifically, as shown, relative to a target trajectory 1143 associatedwith a left lane of lanes 1124.

Sparse map 800 may also include representations of other road-relatedfeatures associated with geographic region 1111. For example, sparse map800 may also include representations of one or more landmarks identifiedin geographic region 1111. Such landmarks may include a first landmark1150 associated with stop line 1132, a second landmark 1152 associatedwith stop sign 1134, a third landmark associated with speed limit sign1154, and a fourth landmark 1156 associated with hazard sign 1138. Suchlandmarks may be used, for example, to assist an autonomous vehicle indetermining its current location relative to any of the shown targettrajectories, such that the vehicle may adjust its heading to match adirection of the target trajectory at the determined location.

In some embodiments, sparse map 800 may also include road signatureprofiles. Such road signature profiles may be associated with anydiscernible/measurable variation in at least one parameter associatedwith a road. For example, in some cases, such profiles may be associatedwith variations in road surface information such as variations insurface roughness of a particular road segment, variations in road widthover a particular road segment, variations in distances between dashedlines painted along a particular road segment, variations in roadcurvature along a particular road segment, etc. FIG. 11D shows anexample of a road signature profile 1160. While profile 1160 mayrepresent any of the parameters mentioned above, or others, in oneexample, profile 1160 may represent a measure of road surface roughness,as obtained, for example, by monitoring one or more sensors providingoutputs indicative of an amount of suspension displacement as a vehicletravels a particular road segment.

Alternatively or concurrently, profile 1160 may represent variation inroad width, as determined based on image data obtained via a cameraonboard a vehicle traveling a particular road segment. Such profiles maybe useful, for example, in determining a particular location of anautonomous vehicle relative to a particular target trajectory. That is,as it traverses a road segment, an autonomous vehicle may measure aprofile associated with one or more parameters associated with the roadsegment. If the measured profile can be correlated/matched with apredetermined profile that plots the parameter variation with respect toposition along the road segment, then the measured and predeterminedprofiles may be used (e.g., by overlaying corresponding sections of themeasured and predetermined profiles) in order to determine a currentposition along the road segment and, therefore, a current positionrelative to a target trajectory for the road segment.

In some embodiments, sparse map 800 may include different trajectoriesbased on different characteristics associated with a user of autonomousvehicles, environmental conditions, and/or other parameters relating todriving. For example, in some embodiments, different trajectories may begenerated based on different user preferences and/or profiles. Sparsemap 800 including such different trajectories may be provided todifferent autonomous vehicles of different users. For example, someusers may prefer to avoid toll roads, while others may prefer to takethe shortest or fastest routes, regardless of whether there is a tollroad on the route. The disclosed systems may generate different sparsemaps with different trajectories based on such different userpreferences or profiles. As another example, some users may prefer totravel in a fast moving lane, while others may prefer to maintain aposition in the central lane at all times.

Different trajectories may be generated and included in sparse map 800based on different environmental conditions, such as day and night,snow, rain, fog, etc. Autonomous vehicles driving under differentenvironmental conditions may be provided with sparse map 800 generatedbased on such different environmental conditions. In some embodiments,cameras provided on autonomous vehicles may detect the environmentalconditions, and may provide such information back to a server thatgenerates and provides sparse maps. For example, the server may generateor update an already generated sparse map 800 to include trajectoriesthat may be more suitable or safer for autonomous driving under thedetected environmental conditions. The update of sparse map 800 based onenvironmental conditions may be performed dynamically as the autonomousvehicles are traveling along roads.

Other different parameters relating to driving may also be used as abasis for generating and providing different sparse maps to differentautonomous vehicles. For example, when an autonomous vehicle istraveling at a high speed, turns may be tighter. Trajectories associatedwith specific lanes, rather than roads, may be included in sparse map800 such that the autonomous vehicle may maintain within a specific laneas the vehicle follows a specific trajectory. When an image captured bya camera onboard the autonomous vehicle indicates that the vehicle hasdrifted outside of the lane (e.g., crossed the lane mark), an action maybe triggered within the vehicle to bring the vehicle back to thedesignated lane according to the specific trajectory.

Crowdsourcing a Sparse Map

In some embodiments, the disclosed systems and methods may generate asparse map for autonomous vehicle navigation. For example, disclosedsystems and methods may use crowdsourced data for generation of a sparsethat one or more autonomous vehicles may use to navigate along a systemof roads. As used herein, “crowdsourcing” means that data are receivedfrom various vehicles (e.g., autonomous vehicles) travelling on a roadsegment at different times, and such data are used to generate and/orupdate the road model. The model may, in turn, be transmitted to thevehicles or other vehicles later travelling along the road segment forassisting autonomous vehicle navigation. The road model may include aplurality of target trajectories representing preferred trajectoriesthat autonomous vehicles should follow as they traverse a road segment.The target trajectories may be the same as a reconstructed actualtrajectory collected from a vehicle traversing a road segment, which maybe transmitted from the vehicle to a server. In some embodiments, thetarget trajectories may be different from actual trajectories that oneor more vehicles previously took when traversing a road segment. Thetarget trajectories may be generated based on actual trajectories (e.g.,through averaging or any other suitable operation).

The vehicle trajectory data that a vehicle may upload to a server maycorrespond with the actual reconstructed trajectory for the vehicle ormay correspond to a recommended trajectory, which may be based on orrelated to the actual reconstructed trajectory of the vehicle, but maydiffer from the actual reconstructed trajectory. For example, vehiclesmay modify their actual, reconstructed trajectories and submit (e.g.,recommend) to the server the modified actual trajectories. The roadmodel may use the recommended, modified trajectories as targettrajectories for autonomous navigation of other vehicles.

In addition to trajectory information, other information for potentialuse in building a sparse data map 800 may include information relatingto potential landmark candidates. For example, through crowd sourcing ofinformation, the disclosed systems and methods may identify potentiallandmarks in an environment and refine landmark positions. The landmarksmay be used by a navigation system of autonomous vehicles to determineand/or adjust the position of the vehicle along the target trajectories.

The reconstructed trajectories that a vehicle may generate as thevehicle travels along a road may be obtained by any suitable method. Insome embodiments, the reconstructed trajectories may be developed bystitching together segments of motion for the vehicle, using, e.g., egomotion estimation (e.g., three dimensional translation and threedimensional rotation of the camera, and hence the body of the vehicle).The rotation and translation estimation may be determined based onanalysis of images captured by one or more image capture devices alongwith information from other sensors or devices, such as inertial sensorsand speed sensors. For example, the inertial sensors may include anaccelerometer or other suitable sensors configured to measure changes intranslation and/or rotation of the vehicle body. The vehicle may includea speed sensor that measures a speed of the vehicle.

In some embodiments, the ego motion of the camera (and hence the vehiclebody) may be estimated based on an optical flow analysis of the capturedimages. An optical flow analysis of a sequence of images identifiesmovement of pixels from the sequence of images, and based on theidentified movement, determines motions of the vehicle. The ego motionmay be integrated over time and along the road segment to reconstruct atrajectory associated with the road segment that the vehicle hasfollowed.

Data (e.g., reconstructed trajectories) collected by multiple vehiclesin multiple drives along a road segment at different times may be usedto construct the road model (e.g., including the target trajectories,etc.) included in sparse data map 800. Data collected by multiplevehicles in multiple drives along a road segment at different times mayalso be averaged to increase an accuracy of the model. In someembodiments, data regarding the road geometry and/or landmarks may bereceived from multiple vehicles that travel through the common roadsegment at different times. Such data received from different vehiclesmay be combined to generate the road model and/or to update the roadmodel.

The geometry of a reconstructed trajectory (and also a targettrajectory) along a road segment may be represented by a curve in threedimensional space, which may be a spline connecting three dimensionalpolynomials. The reconstructed trajectory curve may be determined fromanalysis of a video stream or a plurality of images captured by a camerainstalled on the vehicle. In some embodiments, a location is identifiedin each frame or image that is a few meters ahead of the currentposition of the vehicle. This location is where the vehicle is expectedto travel to in a predetermined time period. This operation may berepeated frame by frame, and at the same time, the vehicle may computethe camera's ego motion (rotation and translation). At each frame orimage, a short range model for the desired path is generated by thevehicle in a reference frame that is attached to the camera. The shortrange models may be stitched together to obtain a three dimensionalmodel of the road in some coordinate frame, which may be an arbitrary orpredetermined coordinate frame. The three dimensional model of the roadmay then be fitted by a spline, which may include or connect one or morepolynomials of suitable orders.

To conclude the short range road model at each frame, one or moredetection modules may be used. For example, a bottom-up lane detectionmodule may be used. The bottom-up lane detection module may be usefulwhen lane marks are drawn on the road. This module may look for edges inthe image and assembles them together to form the lane marks. A secondmodule may be used together with the bottom-up lane detection module.The second module is an end-to-end deep neural network, which may betrained to predict the correct short range path from an input image. Inboth modules, the road model may be detected in the image coordinateframe and transformed to a three dimensional space that may be virtuallyattached to the camera.

Although the reconstructed trajectory modeling method may introduce anaccumulation of errors due to the integration of ego motion over a longperiod of time, which may include a noise component, such errors may beinconsequential as the generated model may provide sufficient accuracyfor navigation over a local scale. In addition, it is possible to cancelthe integrated error by using external sources of information, such assatellite images or geodetic measurements. For example, the disclosedsystems and methods may use a GNSS receiver to cancel accumulatederrors. However, the GNSS positioning signals may not be alwaysavailable and accurate. The disclosed systems and methods may enable asteering application that depends weakly on the availability andaccuracy of GNSS positioning. In such systems, the usage of the GNSSsignals may be limited. For example, in some embodiments, the disclosedsystems may use the GNSS signals for database indexing purposes only.

In some embodiments, the range scale (e.g., local scale) that may berelevant for an autonomous vehicle navigation steering application maybe on the order of 50 meters, 100 meters, 200 meters, 300 meters, etc.Such distances may be used, as the geometrical road model is mainly usedfor two purposes: planning the trajectory ahead and localizing thevehicle on the road model. In some embodiments, the planning task mayuse the model over a typical range of 40 meters ahead (or any othersuitable distance ahead, such as 20 meters, 30 meters, 50 meters), whenthe control algorithm steers the vehicle according to a target pointlocated 1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7seconds, 2 seconds, etc.). The localization task uses the road modelover a typical range of 60 meters behind the car (or any other suitabledistances, such as 50 meters, 100 meters, 150 meters, etc.), accordingto a method called “tail alignment” described in more detail in anothersection. The disclosed systems and methods may generate a geometricalmodel that has sufficient accuracy over particular range, such as 100meters, such that a planned trajectory will not deviate by more than,for example, 30 cm from the lane center.

As explained above, a three dimensional road model may be constructedfrom detecting short range sections and stitching them together. Thestitching may be enabled by computing a six degree ego motion model,using the videos and/or images captured by the camera, data from theinertial sensors that reflect the motions of the vehicle, and the hostvehicle velocity signal. The accumulated error may be small enough oversome local range scale, such as of the order of 100 meters. All this maybe completed in a single drive over a particular road segment.

In some embodiments, multiple drives may be used to average the resultedmodel, and to increase its accuracy further. The same car may travel thesame route multiple times, or multiple cars may send their collectedmodel data to a central server. In any case, a matching procedure may beperformed to identify overlapping models and to enable averaging inorder to generate target trajectories. The constructed model (e.g.,including the target trajectories) may be used for steering once aconvergence criterion is met. Subsequent drives may be used for furthermodel improvements and in order to accommodate infrastructure changes.

Sharing of driving experience (such as sensed data) between multiplecars becomes feasible if they are connected to a central server. Eachvehicle client may store a partial copy of a universal road model, whichmay be relevant for its current position. A bidirectional updateprocedure between the vehicles and the server may be performed by thevehicles and the server. The small footprint concept discussed aboveenables the disclosed systems and methods to perform the bidirectionalupdates using a very small bandwidth.

Information relating to potential landmarks may also be determined andforwarded to a central server. For example, the disclosed systems andmethods may determine one or more physical properties of a potentiallandmark based on one or more images that include the landmark. Thephysical properties may include a physical size (e.g., height, width) ofthe landmark, a distance from a vehicle to a landmark, a distancebetween the landmark to a previous landmark, the lateral position of thelandmark (e.g., the position of the landmark relative to the lane oftravel), the GPS coordinates of the landmark, a type of landmark,identification of text on the landmark, etc. For example, a vehicle mayanalyze one or more images captured by a camera to detect a potentiallandmark, such as a speed limit sign.

The vehicle may determine a distance from the vehicle to the landmarkbased on the analysis of the one or more images. In some embodiments,the distance may be determined based on analysis of images of thelandmark using a suitable image analysis method, such as a scalingmethod and/or an optical flow method. In some embodiments, the disclosedsystems and methods may be configured to determine a type orclassification of a potential landmark. In case the vehicle determinesthat a certain potential landmark corresponds to a predetermined type orclassification stored in a sparse map, it may be sufficient for thevehicle to communicate to the server an indication of the type orclassification of the landmark, along with its location. The server maystore such indications. At a later time, other vehicles may capture animage of the landmark, process the image (e.g., using a classifier), andcompare the result from processing the image to the indication stored inthe server with regard to the type of landmark. There may be varioustypes of landmarks, and different types of landmarks may be associatedwith different types of data to be uploaded to and stored in the server,different processing onboard the vehicle may detects the landmark andcommunicate information about the landmark to the server, and the systemonboard the vehicle may receive the landmark data from the server anduse the landmark data for identifying a landmark in autonomousnavigation.

In some embodiments, multiple autonomous vehicles travelling on a roadsegment may communicate with a server. The vehicles (or clients) maygenerate a curve describing its drive (e.g., through ego motionintegration) in an arbitrary coordinate frame. The vehicles may detectlandmarks and locate them in the same frame. The vehicles may upload thecurve and the landmarks to the server. The server may collect data fromvehicles over multiple drives, and generate a unified road model. Forexample, as discussed below with respect to FIG. 19 , the server maygenerate a sparse map having the unified road model using the uploadedcurves and landmarks.

The server may also distribute the model to clients (e.g., vehicles).For example, the server may distribute the sparse map to one or morevehicles. The server may continuously or periodically update the modelwhen receiving new data from the vehicles. For example, the server mayprocess the new data to evaluate whether the data includes informationthat should trigger an updated, or creation of new data on the server.The server may distribute the updated model or the updates to thevehicles for providing autonomous vehicle navigation.

The server may use one or more criteria for determining whether new datareceived from the vehicles should trigger an update to the model ortrigger creation of new data. For example, when the new data indicatesthat a previously recognized landmark at a specific location no longerexists, or is replaced by another landmark, the server may determinethat the new data should trigger an update to the model. As anotherexample, when the new data indicates that a road segment has beenclosed, and when this has been corroborated by data received from othervehicles, the server may determine that the new data should trigger anupdate to the model.

The server may distribute the updated model (or the updated portion ofthe model) to one or more vehicles that are traveling on the roadsegment, with which the updates to the model are associated. The servermay also distribute the updated model to vehicles that are about totravel on the road segment, or vehicles whose planned trip includes theroad segment, with which the updates to the model are associated. Forexample, while an autonomous vehicle is traveling along another roadsegment before reaching the road segment with which an update isassociated, the server may distribute the updates or updated model tothe autonomous vehicle before the vehicle reaches the road segment.

In some embodiments, the remote server may collect trajectories andlandmarks from multiple clients (e.g., vehicles that travel along acommon road segment). The server may match curves using landmarks andcreate an average road model based on the trajectories collected fromthe multiple vehicles. The server may also compute a graph of roads andthe most probable path at each node or conjunction of the road segment.For example, the remote server may align the trajectories to generate acrowdsourced sparse map from the collected trajectories.

The server may average landmark properties received from multiplevehicles that travelled along the common road segment, such as thedistances between one landmark to another (e.g., a previous one alongthe road segment) as measured by multiple vehicles, to determine anarc-length parameter and support localization along the path and speedcalibration for each client vehicle. The server may average the physicaldimensions of a landmark measured by multiple vehicles travelled alongthe common road segment and recognized the same landmark. The averagedphysical dimensions may be used to support distance estimation, such asthe distance from the vehicle to the landmark. The server may averagelateral positions of a landmark (e.g., position from the lane in whichvehicles are travelling in to the landmark) measured by multiplevehicles travelled along the common road segment and recognized the samelandmark. The averaged lateral potion may be used to support laneassignment. The server may average the GPS coordinates of the landmarkmeasured by multiple vehicles travelled along the same road segment andrecognized the same landmark. The averaged GPS coordinates of thelandmark may be used to support global localization or positioning ofthe landmark in the road model.

In some embodiments, the server may identify model changes, such asconstructions, detours, new signs, removal of signs, etc., based on datareceived from the vehicles. The server may continuously or periodicallyor instantaneously update the model upon receiving new data from thevehicles. The server may distribute updates to the model or the updatedmodel to vehicles for providing autonomous navigation. For example, asdiscussed further below, the server may use crowdsourced data to filterout “ghost” landmarks detected by vehicles.

In some embodiments, the server may analyze driver interventions duringthe autonomous driving. The server may analyze data received from thevehicle at the time and location where intervention occurs, and/or datareceived prior to the time the intervention occurred. The server mayidentify certain portions of the data that caused or are closely relatedto the intervention, for example, data indicating a temporary laneclosure setup, data indicating a pedestrian in the road. The server mayupdate the model based on the identified data. For example, the servermay modify one or more trajectories stored in the model.

FIG. 12 is a schematic illustration of a system that uses crowdsourcingto generate a sparse map (as well as distribute and navigate using acrowdsourced sparse map). FIG. 12 shows a road segment 1200 thatincludes one or more lanes. A plurality of vehicles 1205, 1210, 1215,1220, and 1225 may travel on road segment 1200 at the same time or atdifferent times (although shown as appearing on road segment 1200 at thesame time in FIG. 12 ). At least one of vehicles 1205, 1210, 1215, 1220,and 1225 may be an autonomous vehicle. For simplicity of the presentexample, all of the vehicles 1205, 1210, 1215, 1220, and 1225 arepresumed to be autonomous vehicles.

Each vehicle may be similar to vehicles disclosed in other embodiments(e.g., vehicle 200), and may include components or devices included inor associated with vehicles disclosed in other embodiments. Each vehiclemay be equipped with an image capture device or camera (e.g., imagecapture device 122 or camera 122). Each vehicle may communicate with aremote server 1230 via one or more networks (e.g., over a cellularnetwork and/or the Internet, etc.) through wireless communication paths1235, as indicated by the dashed lines. Each vehicle may transmit datato server 1230 and receive data from server 1230. For example, server1230 may collect data from multiple vehicles travelling on the roadsegment 1200 at different times, and may process the collected data togenerate an autonomous vehicle road navigation model, or an update tothe model. Server 1230 may transmit the autonomous vehicle roadnavigation model or the update to the model to the vehicles thattransmitted data to server 1230. Server 1230 may transmit the autonomousvehicle road navigation model or the update to the model to othervehicles that travel on road segment 1200 at later times.

As vehicles 1205, 1210, 1215, 1220, and 1225 travel on road segment1200, navigation information collected (e.g., detected, sensed, ormeasured) by vehicles 1205, 1210, 1215, 1220, and 1225 may betransmitted to server 1230. In some embodiments, the navigationinformation may be associated with the common road segment 1200. Thenavigation information may include a trajectory associated with each ofthe vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travelsover road segment 1200. In some embodiments, the trajectory may bereconstructed based on data sensed by various sensors and devicesprovided on vehicle 1205. For example, the trajectory may bereconstructed based on at least one of accelerometer data, speed data,landmarks data, road geometry or profile data, vehicle positioning data,and ego motion data. In some embodiments, the trajectory may bereconstructed based on data from inertial sensors, such asaccelerometer, and the velocity of vehicle 1205 sensed by a speedsensor. In addition, in some embodiments, the trajectory may bedetermined (e.g., by a processor onboard each of vehicles 1205, 1210,1215, 1220, and 1225) based on sensed ego motion of the camera, whichmay indicate three dimensional translation and/or three dimensionalrotations (or rotational motions). The ego motion of the camera (andhence the vehicle body) may be determined from analysis of one or moreimages captured by the camera.

In some embodiments, the trajectory of vehicle 1205 may be determined bya processor provided aboard vehicle 1205 and transmitted to server 1230.In other embodiments, server 1230 may receive data sensed by the varioussensors and devices provided in vehicle 1205, and determine thetrajectory based on the data received from vehicle 1205.

In some embodiments, the navigation information transmitted fromvehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may includedata regarding the road surface, the road geometry, or the road profile.The geometry of road segment 1200 may include lane structure and/orlandmarks. The lane structure may include the total number of lanes ofroad segment 1200, the type of lanes (e.g., one-way lane, two-way lane,driving lane, passing lane, etc.), markings on lanes, width of lanes,etc. In some embodiments, the navigation information may include a laneassignment, e.g., which lane of a plurality of lanes a vehicle istraveling in. For example, the lane assignment may be associated with anumerical value “3” indicating that the vehicle is traveling on thethird lane from the left or right. As another example, the laneassignment may be associated with a text value “center lane” indicatingthe vehicle is traveling on the center lane.

Server 1230 may store the navigation information on a non-transitorycomputer-readable medium, such as a hard drive, a compact disc, a tape,a memory, etc. Server 1230 may generate (e.g., through a processorincluded in server 1230) at least a portion of an autonomous vehicleroad navigation model for the common road segment 1200 based on thenavigation information received from the plurality of vehicles 1205,1210, 1215, 1220, and 1225 and may store the model as a portion of asparse map. Server 1230 may determine a trajectory associated with eachlane based on crowdsourced data (e.g., navigation information) receivedfrom multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) thattravel on a lane of road segment at different times. Server 1230 maygenerate the autonomous vehicle road navigation model or a portion ofthe model (e.g., an updated portion) based on a plurality oftrajectories determined based on the crowd sourced navigation data.Server 1230 may transmit the model or the updated portion of the modelto one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225traveling on road segment 1200 or any other autonomous vehicles thattravel on road segment at a later time for updating an existingautonomous vehicle road navigation model provided in a navigation systemof the vehicles. The autonomous vehicle road navigation model may beused by the autonomous vehicles in autonomously navigating along thecommon road segment 1200.

As explained above, the autonomous vehicle road navigation model may beincluded in a sparse map (e.g., sparse map 800 depicted in FIG. 8 ).Sparse map 800 may include sparse recording of data related to roadgeometry and/or landmarks along a road, which may provide sufficientinformation for guiding autonomous navigation of an autonomous vehicle,yet does not require excessive data storage. In some embodiments, theautonomous vehicle road navigation model may be stored separately fromsparse map 800, and may use map data from sparse map 800 when the modelis executed for navigation. In some embodiments, the autonomous vehicleroad navigation model may use map data included in sparse map 800 fordetermining target trajectories along road segment 1200 for guidingautonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and1225 or other vehicles that later travel along road segment 1200. Forexample, when the autonomous vehicle road navigation model is executedby a processor included in a navigation system of vehicle 1205, themodel may cause the processor to compare the trajectories determinedbased on the navigation information received from vehicle 1205 withpredetermined trajectories included in sparse map 800 to validate and/orcorrect the current traveling course of vehicle 1205.

In the autonomous vehicle road navigation model, the geometry of a roadfeature or target trajectory may be encoded by a curve in athree-dimensional space. In one embodiment, the curve may be a threedimensional spline including one or more connecting three dimensionalpolynomials. As one of skill in the art would understand, a spline maybe a numerical function that is piece-wise defined by a series ofpolynomials for fitting data. A spline for fitting the three dimensionalgeometry data of the road may include a linear spline (first order), aquadratic spline (second order), a cubic spline (third order), or anyother splines (other orders), or a combination thereof. The spline mayinclude one or more three dimensional polynomials of different ordersconnecting (e.g., fitting) data points of the three dimensional geometrydata of the road. In some embodiments, the autonomous vehicle roadnavigation model may include a three dimensional spline corresponding toa target trajectory along a common road segment (e.g., road segment1200) or a lane of the road segment 1200.

As explained above, the autonomous vehicle road navigation modelincluded in the sparse map may include other information, such asidentification of at least one landmark along road segment 1200. Thelandmark may be visible within a field of view of a camera (e.g., camera122) installed on each of vehicles 1205, 1210, 1215, 1220, and 1225. Insome embodiments, camera 122 may capture an image of a landmark. Aprocessor (e.g., processor 180, 190, or processing unit 110) provided onvehicle 1205 may process the image of the landmark to extractidentification information for the landmark. The landmark identificationinformation, rather than an actual image of the landmark, may be storedin sparse map 800. The landmark identification information may requiremuch less storage space than an actual image. Other sensors or systems(e.g., GPS system) may also provide certain identification informationof the landmark (e.g., position of landmark). The landmark may includeat least one of a traffic sign, an arrow marking, a lane marking, adashed lane marking, a traffic light, a stop line, a directional sign(e.g., a highway exit sign with an arrow indicating a direction, ahighway sign with arrows pointing to different directions or places), alandmark beacon, or a lamppost. A landmark beacon refers to a device(e.g., an RFID device) installed along a road segment that transmits orreflects a signal to a receiver installed on a vehicle, such that whenthe vehicle passes by the device, the beacon received by the vehicle andthe location of the device (e.g., determined from GPS location of thedevice) may be used as a landmark to be included in the autonomousvehicle road navigation model and/or the sparse map 800.

The identification of at least one landmark may include a position ofthe at least one landmark. The position of the landmark may bedetermined based on position measurements performed using sensor systems(e.g., Global Positioning Systems, inertial based positioning systems,landmark beacon, etc.) associated with the plurality of vehicles 1205,1210, 1215, 1220, and 1225. In some embodiments, the position of thelandmark may be determined by averaging the position measurementsdetected, collected, or received by sensor systems on different vehicles1205, 1210, 1215, 1220, and 1225 through multiple drives. For example,vehicles 1205, 1210, 1215, 1220, and 1225 may transmit positionmeasurements data to server 1230, which may average the positionmeasurements and use the averaged position measurement as the positionof the landmark. The position of the landmark may be continuouslyrefined by measurements received from vehicles in subsequent drives.

The identification of the landmark may include a size of the landmark.The processor provided on a vehicle (e.g., 1205) may estimate thephysical size of the landmark based on the analysis of the images.Server 1230 may receive multiple estimates of the physical size of thesame landmark from different vehicles over different drives. Server 1230may average the different estimates to arrive at a physical size for thelandmark, and store that landmark size in the road model. The physicalsize estimate may be used to further determine or estimate a distancefrom the vehicle to the landmark. The distance to the landmark may beestimated based on the current speed of the vehicle and a scale ofexpansion based on the position of the landmark appearing in the imagesrelative to the focus of expansion of the camera. For example, thedistance to landmark may be estimated by Z=V*dt*R/D, where V is thespeed of vehicle, R is the distance in the image from the landmark attime t1 to the focus of expansion, and D is the change in distance forthe landmark in the image from t1 to t2. dt represents the (t2−t1). Forexample, the distance to landmark may be estimated by Z=V*dt*R/D, whereV is the speed of vehicle, R is the distance in the image between thelandmark and the focus of expansion, dt is a time interval, and D is theimage displacement of the landmark along the epipolar line. Otherequations equivalent to the above equation, such as Z=V*ω/Δω, may beused for estimating the distance to the landmark. Here, V is the vehiclespeed, ω is an image length (like the object width), and Δω is thechange of that image length in a unit of time.

When the physical size of the landmark is known, the distance to thelandmark may also be determined based on the following equation:Z=f*W/ω), where f is the focal length, W is the size of the landmark(e.g., height or width), w is the number of pixels when the landmarkleaves the image. From the above equation, a change in distance Z may becalculated using ΔZ=f*W*Δω/ω²+f*ΔW/ω, where ΔW decays to zero byaveraging, and where Δω is the number of pixels representing a boundingbox accuracy in the image. A value estimating the physical size of thelandmark may be calculated by averaging multiple observations at theserver side. The resulting error in distance estimation may be verysmall. There are two sources of error that may occur when using theformula above, namely ΔW and Δω. Their contribution to the distanceerror is given by ΔZ=f*W*Δω/ω²+f*ΔW/w. However, ΔW decays to zero byaveraging; hence ΔZ is determined by Δω (e.g., the inaccuracy of thebounding box in the image).

For landmarks of unknown dimensions, the distance to the landmark may beestimated by tracking feature points on the landmark between successiveframes. For example, certain features appearing on a speed limit signmay be tracked between two or more image frames. Based on these trackedfeatures, a distance distribution per feature point may be generated.The distance estimate may be extracted from the distance distribution.For example, the most frequent distance appearing in the distancedistribution may be used as the distance estimate. As another example,the average of the distance distribution may be used as the distanceestimate.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines 1301, 1302, and1303. The curves 1301, 1302, and 1303 shown in FIG. 13 are forillustration purpose only. Each spline may include one or more threedimensional polynomials connecting a plurality of data points 1310. Eachpolynomial may be a first order polynomial, a second order polynomial, athird order polynomial, or a combination of any suitable polynomialshaving different orders. Each data point 1310 may be associated with thenavigation information received from vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, each data point 1310 may be associatedwith data related to landmarks (e.g., size, location, and identificationinformation of landmarks) and/or road signature profiles (e.g., roadgeometry, road roughness profile, road curvature profile, road widthprofile). In some embodiments, some data points 1310 may be associatedwith data related to landmarks, and others may be associated with datarelated to road signature profiles.

FIG. 14 illustrates raw location data 1410 (e.g., GPS data) receivedfrom five separate drives. One drive may be separate from another driveif it was traversed by separate vehicles at the same time, by the samevehicle at separate times, or by separate vehicles at separate times. Toaccount for errors in the location data 1410 and for differing locationsof vehicles within the same lane (e.g., one vehicle may drive closer tothe left of a lane than another), server 1230 may generate a mapskeleton 1420 using one or more statistical techniques to determinewhether variations in the raw location data 1410 represent actualdivergences or statistical errors. Each path within skeleton 1420 may belinked back to the raw data 1410 that formed the path. For example, thepath between A and B within skeleton 1420 is linked to raw data 1410from drives 2, 3, 4, and 5 but not from drive 1. Skeleton 1420 may notbe detailed enough to be used to navigate a vehicle (e.g., because itcombines drives from multiple lanes on the same road unlike the splinesdescribed above) but may provide useful topological information and maybe used to define intersections.

FIG. 15 illustrates an example by which additional detail may begenerated for a sparse map within a segment of a map skeleton (e.g.,segment A to B within skeleton 1420). As depicted in FIG. 15 , the data(e.g. ego-motion data, road markings data, and the like) may be shown asa function of position S (or S₁ or S₂) along the drive. Server 1230 mayidentify landmarks for the sparse map by identifying unique matchesbetween landmarks 1501, 1503, and 1505 of drive 1510 and landmarks 1507and 1509 of drive 1520. Such a matching algorithm may result inidentification of landmarks 1511, 1513, and 1515. One skilled in the artwould recognize, however, that other matching algorithms may be used.For example, probability optimization may be used in lieu of or incombination with unique matching. Server 1230 may longitudinally alignthe drives to align the matched landmarks. For example, server 1230 mayselect one drive (e.g., drive 1520) as a reference drive and then shiftand/or elastically stretch the other drive(s) (e.g., drive 1510) foralignment.

FIG. 16 shows an example of aligned landmark data for use in a sparsemap. In the example of FIG. 16 , landmark 1610 comprises a road sign.The example of FIG. 16 further depicts data from a plurality of drives1601, 1603, 1605, 1607, 1609, 1611, and 1613. In the example of FIG. 16, the data from drive 1613 consists of a “ghost” landmark, and theserver 1230 may identify it as such because none of drives 1601, 1603,1605, 1607, 1609, and 1611 include an identification of a landmark inthe vicinity of the identified landmark in drive 1613. Accordingly,server 1230 may accept potential landmarks when a ratio of images inwhich the landmark does appear to images in which the landmark does notappear exceeds a threshold and/or may reject potential landmarks when aratio of images in which the landmark does not appear to images in whichthe landmark does appear exceeds a threshold.

FIG. 17 depicts a system 1700 for generating drive data, which may beused to crowdsource a sparse map. As depicted in FIG. 17 , system 1700may include a camera 1701 and a locating device 1703 (e.g., a GPSlocator). Camera 1701 and locating device 1703 may be mounted on avehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera1701 may produce a plurality of data of multiple types, e.g., ego motiondata, traffic sign data, road data, or the like. The camera data andlocation data may be segmented into drive segments 1705. For example,drive segments 1705 may each have camera data and location data fromless than 1 km of driving.

In some embodiments, system 1700 may remove redundancies in drivesegments 1705. For example, if a landmark appears in multiple imagesfrom camera 1701, system 1700 may strip the redundant data such that thedrive segments 1705 only contain one copy of the location of and anymetadata relating to the landmark. By way of further example, if a lanemarking appears in multiple images from camera 1701, system 1700 maystrip the redundant data such that the drive segments 1705 only containone copy of the location of and any metadata relating to the lanemarking.

System 1700 also includes a server (e.g., server 1230). Server 1230 mayreceive drive segments 1705 from the vehicle and recombine the drivesegments 1705 into a single drive 1707. Such an arrangement may allowfor reduce bandwidth requirements when transferring data between thevehicle and the server while also allowing for the server to store datarelating to an entire drive.

FIG. 18 depicts system 1700 of FIG. 17 further configured forcrowdsourcing a sparse map. As in FIG. 17 , system 1700 includes vehicle1810, which captures drive data using, for example, a camera (whichproduces, e.g., ego motion data, traffic sign data, road data, or thelike) and a locating device (e.g., a GPS locator). As in FIG. 17 ,vehicle 1810 segments the collected data into drive segments (depictedas “DS1 1,” “DS2 1,” “DSN 1” in FIG. 18 ). Server 1230 then receives thedrive segments and reconstructs a drive (depicted as “Drive 1” in FIG.18 ) from the received segments.

As further depicted in FIG. 18 , system 1700 also receives data fromadditional vehicles. For example, vehicle 1820 also captures drive datausing, for example, a camera (which produces, e.g., ego motion data,traffic sign data, road data, or the like) and a locating device (e.g.,a GPS locator). Similar to vehicle 1810, vehicle 1820 segments thecollected data into drive segments (depicted as “DS1 2,” “DS2 2,” “DSN2” in FIG. 18 ). Server 1230 then receives the drive segments andreconstructs a drive (depicted as “Drive 2” in FIG. 18 ) from thereceived segments. Any number of additional vehicles may be used. Forexample, FIG. 18 also includes “CAR N” that captures drive data,segments it into drive segments (depicted as “DS1 N,” “DS2 N,” “DSN N”in FIG. 18 ), and sends it to server 1230 for reconstruction into adrive (depicted as “Drive N” in FIG. 18 ).

As depicted in FIG. 18 , server 1230 may construct a sparse map(depicted as “MAP”) using the reconstructed drives (e.g., “Drive 1,”“Drive 2,” and “Drive N”) collected from a plurality of vehicles (e.g.,“CAR 1” (also labeled vehicle 1810), “CAR 2” (also labeled vehicle1820), and “CAR N”).

FIG. 19 is a flowchart showing an example process 1900 for generating asparse map for autonomous vehicle navigation along a road segment.Process 1900 may be performed by one or more processing devices includedin server 1230.

Process 1900 may include receiving a plurality of images acquired as oneor more vehicles traverse the road segment (step 1905). Server 1230 mayreceive images from cameras included within one or more of vehicles1205, 1210, 1215, 1220, and 1225. For example, camera 122 may captureone or more images of the environment surrounding vehicle 1205 asvehicle 1205 travels along road segment 1200. In some embodiments,server 1230 may also receive stripped down image data that has hadredundancies removed by a processor on vehicle 1205, as discussed abovewith respect to FIG. 17 .

Process 1900 may further include identifying, based on the plurality ofimages, at least one line representation of a road surface featureextending along the road segment (step 1910). Each line representationmay represent a path along the road segment substantially correspondingwith the road surface feature. For example, server 1230 may analyze theenvironmental images received from camera 122 to identify a road edge ora lane marking and determine a trajectory of travel along road segment1200 associated with the road edge or lane marking. In some embodiments,the trajectory (or line representation) may include a spline, apolynomial representation, or a curve. Server 1230 may determine thetrajectory of travel of vehicle 1205 based on camera ego motions (e.g.,three dimensional translation and/or three dimensional rotationalmotions) received at step 1905.

Process 1900 may also include identifying, based on the plurality ofimages, a plurality of landmarks associated with the road segment (step1910). For example, server 1230 may analyze the environmental imagesreceived from camera 122 to identify one or more landmarks, such as roadsign along road segment 1200. Server 1230 may identify the landmarksusing analysis of the plurality of images acquired as one or morevehicles traverse the road segment. To enable crowdsourcing, theanalysis may include rules regarding accepting and rejecting possiblelandmarks associated with the road segment. For example, the analysismay include accepting potential landmarks when a ratio of images inwhich the landmark does appear to images in which the landmark does notappear exceeds a threshold and/or rejecting potential landmarks when aratio of images in which the landmark does not appear to images in whichthe landmark does appear exceeds a threshold.

Process 1900 may include other operations or steps performed by server1230. For example, the navigation information may include a targettrajectory for vehicles to travel along a road segment, and process 1900may include clustering, by server 1230, vehicle trajectories related tomultiple vehicles travelling on the road segment and determining thetarget trajectory based on the clustered vehicle trajectories, asdiscussed in further detail below. Clustering vehicle trajectories mayinclude clustering, by server 1230, the multiple trajectories related tothe vehicles travelling on the road segment into a plurality of clustersbased on at least one of the absolute heading of vehicles or laneassignment of the vehicles. Generating the target trajectory may includeaveraging, by server 1230, the clustered trajectories. By way of furtherexample, process 1900 may include aligning data received in step 1905.Other processes or steps performed by server 1230, as described above,may also be included in process 1900.

The disclosed systems and methods may include other features. Forexample, the disclosed systems may use local coordinates, rather thanglobal coordinates. For autonomous driving, some systems may presentdata in world coordinates. For example, longitude and latitudecoordinates on the earth surface may be used. In order to use the mapfor steering, the host vehicle may determine its position andorientation relative to the map. It seems natural to use a GPS device onboard, in order to position the vehicle on the map and in order to findthe rotation transformation between the body reference frame and theworld reference frame (e.g., North, East and Down). Once the bodyreference frame is aligned with the map reference frame, then thedesired route may be expressed in the body reference frame and thesteering commands may be computed or generated.

The disclosed systems and methods may enable autonomous vehiclenavigation (e.g., steering control) with low footprint models, which maybe collected by the autonomous vehicles themselves without the aid ofexpensive surveying equipment. To support the autonomous navigation(e.g., steering applications), the road model may include a sparse maphaving the geometry of the road, its lane structure, and landmarks thatmay be used to determine the location or position of vehicles along atrajectory included in the model. As discussed above, generation of thesparse map may be performed by a remote server that communicates withvehicles travelling on the road and that receives data from thevehicles. The data may include sensed data, trajectories reconstructedbased on the sensed data, and/or recommended trajectories that mayrepresent modified reconstructed trajectories. As discussed below, theserver may transmit the model back to the vehicles or other vehiclesthat later travel on the road to aid in autonomous navigation.

FIG. 20 illustrates a block diagram of server 1230. Server 1230 mayinclude a communication unit 2005, which may include both hardwarecomponents (e.g., communication control circuits, switches, andantenna), and software components (e.g., communication protocols,computer codes). For example, communication unit 2005 may include atleast one network interface. Server 1230 may communicate with vehicles1205, 1210, 1215, 1220, and 1225 through communication unit 2005. Forexample, server 1230 may receive, through communication unit 2005,navigation information transmitted from vehicles 1205, 1210, 1215, 1220,and 1225. Server 1230 may distribute, through communication unit 2005,the autonomous vehicle road navigation model to one or more autonomousvehicles.

Server 1230 may include at least one non-transitory storage medium 2010,such as a hard drive, a compact disc, a tape, etc. Storage device 1410may be configured to store data, such as navigation information receivedfrom vehicles 1205, 1210, 1215, 1220, and 1225 and/or the autonomousvehicle road navigation model that server 1230 generates based on thenavigation information. Storage device 2010 may be configured to storeany other information, such as a sparse map (e.g., sparse map 800discussed above with respect to FIG. 8 ).

In addition to or in place of storage device 2010, server 1230 mayinclude a memory 2015. Memory 2015 may be similar to or different frommemory 140 or 150. Memory 2015 may be a non-transitory memory, such as aflash memory, a random access memory, etc. Memory 2015 may be configuredto store data, such as computer codes or instructions executable by aprocessor (e.g., processor 2020), map data (e.g., data of sparse map800), the autonomous vehicle road navigation model, and/or navigationinformation received from vehicles 1205, 1210, 1215, 1220, and 1225.

Server 1230 may include at least one processing device 2020 configuredto execute computer codes or instructions stored in memory 2015 toperform various functions. For example, processing device 2020 mayanalyze the navigation information received from vehicles 1205, 1210,1215, 1220, and 1225, and generate the autonomous vehicle roadnavigation model based on the analysis. Processing device 2020 maycontrol communication unit 1405 to distribute the autonomous vehicleroad navigation model to one or more autonomous vehicles (e.g., one ormore of vehicles 1205, 1210, 1215, 1220, and 1225 or any vehicle thattravels on road segment 1200 at a later time). Processing device 2020may be similar to or different from processor 180, 190, or processingunit 110.

FIG. 21 illustrates a block diagram of memory 2015, which may storecomputer code or instructions for performing one or more operations forgenerating a road navigation model for use in autonomous vehiclenavigation. As shown in FIG. 21 , memory 2015 may store one or moremodules for performing the operations for processing vehicle navigationinformation. For example, memory 2015 may include a model generatingmodule 2105 and a model distributing module 2110. Processor 2020 mayexecute the instructions stored in any of modules 2105 and 2110 includedin memory 2015.

Model generating module 2105 may store instructions which, when executedby processor 2020, may generate at least a portion of an autonomousvehicle road navigation model for a common road segment (e.g., roadsegment 1200) based on navigation information received from vehicles1205, 1210, 1215, 1220, and 1225. For example, in generating theautonomous vehicle road navigation model, processor 2020 may clustervehicle trajectories along the common road segment 1200 into differentclusters. Processor 2020 may determine a target trajectory along thecommon road segment 1200 based on the clustered vehicle trajectories foreach of the different clusters. Such an operation may include finding amean or average trajectory of the clustered vehicle trajectories (e.g.,by averaging data representing the clustered vehicle trajectories) ineach cluster. In some embodiments, the target trajectory may beassociated with a single lane of the common road segment 1200.

The road model and/or sparse map may store trajectories associated witha road segment. These trajectories may be referred to as targettrajectories, which are provided to autonomous vehicles for autonomousnavigation. The target trajectories may be received from multiplevehicles, or may be generated based on actual trajectories orrecommended trajectories (actual trajectories with some modifications)received from multiple vehicles. The target trajectories included in theroad model or sparse map may be continuously updated (e.g., averaged)with new trajectories received from other vehicles.

Vehicles travelling on a road segment may collect data by varioussensors. The data may include landmarks, road signature profile, vehiclemotion (e.g., accelerometer data, speed data), vehicle position (e.g.,GPS data), and may either reconstruct the actual trajectoriesthemselves, or transmit the data to a server, which will reconstruct theactual trajectories for the vehicles. In some embodiments, the vehiclesmay transmit data relating to a trajectory (e.g., a curve in anarbitrary reference frame), landmarks data, and lane assignment alongtraveling path to server 1230. Various vehicles travelling along thesame road segment at multiple drives may have different trajectories.Server 1230 may identify routes or trajectories associated with eachlane from the trajectories received from vehicles through a clusteringprocess.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles 1205, 1210, 1215, 1220, and 1225 fordetermining a target trajectory for the common road segment (e.g., roadsegment 1200). The target trajectory or a plurality of targettrajectories determined from the clustering process may be included inthe autonomous vehicle road navigation model or sparse map 800. In someembodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling alongroad segment 1200 may transmit a plurality of trajectories 2200 toserver 1230. In some embodiments, server 1230 may generate trajectoriesbased on landmark, road geometry, and vehicle motion informationreceived from vehicles 1205, 1210, 1215, 1220, and 1225. To generate theautonomous vehicle road navigation model, server 1230 may clustervehicle trajectories 1600 into a plurality of clusters 2205, 2210, 2215,2220, 2225, and 2230, as shown in FIG. 22 .

Clustering may be performed using various criteria. In some embodiments,all drives in a cluster may be similar with respect to the absoluteheading along the road segment 1200. The absolute heading may beobtained from GPS signals received by vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, the absolute heading may be obtainedusing dead reckoning. Dead reckoning, as one of skill in the art wouldunderstand, may be used to determine the current position and henceheading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previouslydetermined position, estimated speed, etc. Trajectories clustered byabsolute heading may be useful for identifying routes along theroadways.

In some embodiments, all the drives in a cluster may be similar withrespect to the lane assignment (e.g., in the same lane before and aftera junction) along the drive on road segment 1200. Trajectories clusteredby lane assignment may be useful for identifying lanes along theroadways. In some embodiments, both criteria (e.g., absolute heading andlane assignment) may be used for clustering.

In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, trajectories maybe averaged to obtain a target trajectory associated with the specificcluster. For example, the trajectories from multiple drives associatedwith the same lane cluster may be averaged. The averaged trajectory maybe a target trajectory associate with a specific lane. To average acluster of trajectories, server 1230 may select a reference frame of anarbitrary trajectory C0. For all other trajectories (C1, Cn), server1230 may find a rigid transformation that maps Ci to C0, where i=1, 2, .. . , n, where n is a positive integer number, corresponding to thetotal number of trajectories included in the cluster. Server 1230 maycompute a mean curve or trajectory in the C0 reference frame.

In some embodiments, the landmarks may define an arc length matchingbetween different drives, which may be used for alignment oftrajectories with lanes. In some embodiments, lane marks before andafter a junction may be used for alignment of trajectories with lanes.

To assemble lanes from the trajectories, server 1230 may select areference frame of an arbitrary lane. Server 1230 may map partiallyoverlapping lanes to the selected reference frame. Server 1230 maycontinue mapping until all lanes are in the same reference frame. Lanesthat are next to each other may be aligned as if they were the samelane, and later they may be shifted laterally.

Landmarks recognized along the road segment may be mapped to the commonreference frame, first at the lane level, then at the junction level.For example, the same landmarks may be recognized multiple times bymultiple vehicles in multiple drives. The data regarding the samelandmarks received in different drives may be slightly different. Suchdata may be averaged and mapped to the same reference frame, such as theC0 reference frame. Additionally or alternatively, the variance of thedata of the same landmark received in multiple drives may be calculated.

In some embodiments, each lane of road segment 120 may be associatedwith a target trajectory and certain landmarks. The target trajectory ora plurality of such target trajectories may be included in theautonomous vehicle road navigation model, which may be used later byother autonomous vehicles travelling along the same road segment 1200.Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 whilethe vehicles travel along road segment 1200 may be recorded inassociation with the target trajectory. The data of the targettrajectories and landmarks may be continuously or periodically updatedwith new data received from other vehicles in subsequent drives.

For localization of an autonomous vehicle, the disclosed systems andmethods may use an Extended Kalman Filter. The location of the vehiclemay be determined based on three dimensional position data and/or threedimensional orientation data, prediction of future location ahead ofvehicle's current location by integration of ego motion. Thelocalization of vehicle may be corrected or adjusted by imageobservations of landmarks. For example, when vehicle detects a landmarkwithin an image captured by the camera, the landmark may be compared toa known landmark stored within the road model or sparse map 800. Theknown landmark may have a known location (e.g., GPS data) along a targettrajectory stored in the road model and/or sparse map 800. Based on thecurrent speed and images of the landmark, the distance from the vehicleto the landmark may be estimated. The location of the vehicle along atarget trajectory may be adjusted based on the distance to the landmarkand the landmark's known location (stored in the road model or sparsemap 800). The landmark's position/location data (e.g., mean values frommultiple drives) stored in the road model and/or sparse map 800 may bepresumed to be accurate.

In some embodiments, the disclosed system may form a closed loopsubsystem, in which estimation of the vehicle six degrees of freedomlocation (e.g., three dimensional position data plus three dimensionalorientation data) may be used for navigating (e.g., steering the wheelof) the autonomous vehicle to reach a desired point (e.g., 1.3 secondahead in the stored). In turn, data measured from the steering andactual navigation may be used to estimate the six degrees of freedomlocation.

In some embodiments, poles along a road, such as lampposts and power orcable line poles may be used as landmarks for localizing the vehicles.Other landmarks such as traffic signs, traffic lights, arrows on theroad, stop lines, as well as static features or signatures of an objectalong the road segment may also be used as landmarks for localizing thevehicle. When poles are used for localization, the x observation of thepoles (i.e., the viewing angle from the vehicle) may be used, ratherthan the y observation (i.e., the distance to the pole) since thebottoms of the poles may be occluded and sometimes they are not on theroad plane.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation using a crowdsourced sparse map. Forillustration, the vehicle is referenced as vehicle 1205. The vehicleshown in FIG. 23 may be any other vehicle disclosed herein, including,for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200shown in other embodiments. As shown in FIG. 12 , vehicle 1205 maycommunicate with server 1230. Vehicle 1205 may include an image capturedevice 122 (e.g., camera 122). Vehicle 1205 may include a navigationsystem 2300 configured for providing navigation guidance for vehicle1205 to travel on a road (e.g., road segment 1200). Vehicle 1205 mayalso include other sensors, such as a speed sensor 2320 and anaccelerometer 2325. Speed sensor 2320 may be configured to detect thespeed of vehicle 1205. Accelerometer 2325 may be configured to detect anacceleration or deceleration of vehicle 1205. Vehicle 1205 shown in FIG.23 may be an autonomous vehicle, and the navigation system 2300 may beused for providing navigation guidance for autonomous driving.Alternatively, vehicle 1205 may also be a non-autonomous,human-controlled vehicle, and navigation system 2300 may still be usedfor providing navigation guidance.

Navigation system 2300 may include a communication unit 2305 configuredto communicate with server 1230 through communication path 1235.Navigation system 2300 may also include a GPS unit 2310 configured toreceive and process GPS signals. Navigation system 2300 may furtherinclude at least one processor 2315 configured to process data, such asGPS signals, map data from sparse map 800 (which may be stored on astorage device provided onboard vehicle 1205 and/or received from server1230), road geometry sensed by a road profile sensor 2330, imagescaptured by camera 122, and/or autonomous vehicle road navigation modelreceived from server 1230. The road profile sensor 2330 may includedifferent types of devices for measuring different types of roadprofile, such as road surface roughness, road width, road elevation,road curvature, etc. For example, the road profile sensor 2330 mayinclude a device that measures the motion of a suspension of vehicle2305 to derive the road roughness profile. In some embodiments, the roadprofile sensor 2330 may include radar sensors to measure the distancefrom vehicle 1205 to road sides (e.g., barrier on the road sides),thereby measuring the width of the road. In some embodiments, the roadprofile sensor 2330 may include a device configured for measuring the upand down elevation of the road. In some embodiment, the road profilesensor 2330 may include a device configured to measure the roadcurvature. For example, a camera (e.g., camera 122 or another camera)may be used to capture images of the road showing road curvatures.Vehicle 1205 may use such images to detect road curvatures.

The at least one processor 2315 may be programmed to receive, fromcamera 122, at least one environmental image associated with vehicle1205. The at least one processor 2315 may analyze the at least oneenvironmental image to determine navigation information related to thevehicle 1205. The navigation information may include a trajectoryrelated to the travel of vehicle 1205 along road segment 1200. The atleast one processor 2315 may determine the trajectory based on motionsof camera 122 (and hence the vehicle), such as three dimensionaltranslation and three dimensional rotational motions. In someembodiments, the at least one processor 2315 may determine thetranslation and rotational motions of camera 122 based on analysis of aplurality of images acquired by camera 122. In some embodiments, thenavigation information may include lane assignment information (e.g., inwhich lane vehicle 1205 is travelling along road segment 1200). Thenavigation information transmitted from vehicle 1205 to server 1230 maybe used by server 1230 to generate and/or update an autonomous vehicleroad navigation model, which may be transmitted back from server 1230 tovehicle 1205 for providing autonomous navigation guidance for vehicle1205.

The at least one processor 2315 may also be programmed to transmit thenavigation information from vehicle 1205 to server 1230. In someembodiments, the navigation information may be transmitted to server1230 along with road information. The road location information mayinclude at least one of the GPS signal received by the GPS unit 2310,landmark information, road geometry, lane information, etc. The at leastone processor 2315 may receive, from server 1230, the autonomous vehicleroad navigation model or a portion of the model. The autonomous vehicleroad navigation model received from server 1230 may include at least oneupdate based on the navigation information transmitted from vehicle 1205to server 1230. The portion of the model transmitted from server 1230 tovehicle 1205 may include an updated portion of the model. The at leastone processor 2315 may cause at least one navigational maneuver (e.g.,steering such as making a turn, braking, accelerating, passing anothervehicle, etc.) by vehicle 1205 based on the received autonomous vehicleroad navigation model or the updated portion of the model.

The at least one processor 2315 may be configured to communicate withvarious sensors and components included in vehicle 1205, includingcommunication unit 1705, GPS unit 2315, camera 122, speed sensor 2320,accelerometer 2325, and road profile sensor 2330. The at least oneprocessor 2315 may collect information or data from various sensors andcomponents, and transmit the information or data to server 1230 throughcommunication unit 2305. Alternatively or additionally, various sensorsor components of vehicle 1205 may also communicate with server 1230 andtransmit data or information collected by the sensors or components toserver 1230.

In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 maycommunicate with each other, and may share navigation information witheach other, such that at least one of the vehicles 1205, 1210, 1215,1220, and 1225 may generate the autonomous vehicle road navigation modelusing crowdsourcing, e.g., based on information shared by othervehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225may share navigation information with each other and each vehicle mayupdate its own the autonomous vehicle road navigation model provided inthe vehicle. In some embodiments, at least one of the vehicles 1205,1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may function as a hubvehicle. The at least one processor 2315 of the hub vehicle (e.g.,vehicle 1205) may perform some or all of the functions performed byserver 1230. For example, the at least one processor 2315 of the hubvehicle may communicate with other vehicles and receive navigationinformation from other vehicles. The at least one processor 2315 of thehub vehicle may generate the autonomous vehicle road navigation model oran update to the model based on the shared information received fromother vehicles. The at least one processor 2315 of the hub vehicle maytransmit the autonomous vehicle road navigation model or the update tothe model to other vehicles for providing autonomous navigationguidance.

Mapping Lane Marks and Navigation Based on Mapped Lane Marks

As previously discussed, the autonomous vehicle road navigation modeland/or sparse map 800 may include a plurality of mapped lane marksassociated with a road segment. As discussed in greater detail below,these mapped lane marks may be used when the autonomous vehiclenavigates. For example, in some embodiments, the mapped lane marks maybe used to determine a lateral position and/or orientation relative to aplanned trajectory. With this position information, the autonomousvehicle may be able to adjust a heading direction to match a directionof a target trajectory at the determined position.

Vehicle 200 may be configured to detect lane marks in a given roadsegment. The road segment may include any markings on a road for guidingvehicle traffic on a roadway. For example, the lane marks may becontinuous or dashed lines demarking the edge of a lane of travel. Thelane marks may also include double lines, such as a double continuouslines, double dashed lines or a combination of continuous and dashedlines indicating, for example, whether passing is permitted in anadjacent lane. The lane marks may also include freeway entrance and exitmarkings indicating, for example, a deceleration lane for an exit rampor dotted lines indicating that a lane is turn-only or that the lane isending. The markings may further indicate a work zone, a temporary laneshift, a path of travel through an intersection, a median, a specialpurpose lane (e.g., a bike lane, HOV lane, etc.), or other miscellaneousmarkings (e.g., crosswalk, a speed hump, a railway crossing, a stopline, etc.).

Vehicle 200 may use cameras, such as image capture devices 122 and 124included in image acquisition unit 120, to capture images of thesurrounding lane marks. Vehicle 200 may analyze the images to detectpoint locations associated with the lane marks based on featuresidentified within one or more of the captured images. These pointlocations may be uploaded to a server to represent the lane marks insparse map 800. Depending on the position and field of view of thecamera, lane marks may be detected for both sides of the vehiclesimultaneously from a single image. In other embodiments, differentcameras may be used to capture images on multiple sides of the vehicle.Rather than uploading actual images of the lane marks, the marks may bestored in sparse map 800 as a spline or a series of points, thusreducing the size of sparse map 800 and/or the data that must beuploaded remotely by the vehicle.

FIGS. 24A-24D illustrate exemplary point locations that may be detectedby vehicle 200 to represent particular lane marks. Similar to thelandmarks described above, vehicle 200 may use various image recognitionalgorithms or software to identify point locations within a capturedimage. For example, vehicle 200 may recognize a series of edge points,corner points or various other point locations associated with aparticular lane mark. FIG. 24A shows a continuous lane mark 2410 thatmay be detected by vehicle 200. Lane mark 2410 may represent the outsideedge of a roadway, represented by a continuous white line. As shown inFIG. 24A, vehicle 200 may be configured to detect a plurality of edgelocation points 2411 along the lane mark. Location points 2411 may becollected to represent the lane mark at any intervals sufficient tocreate a mapped lane mark in the sparse map. For example, the lane markmay be represented by one point per meter of the detected edge, onepoint per every five meters of the detected edge, or at other suitablespacings. In some embodiments, the spacing may be determined by otherfactors, rather than at set intervals such as, for example, based onpoints where vehicle 200 has a highest confidence ranking of thelocation of the detected points. Although FIG. 24A shows edge locationpoints on an interior edge of lane mark 2410, points may be collected onthe outside edge of the line or along both edges. Further, while asingle line is shown in FIG. 24A, similar edge points may be detectedfor a double continuous line. For example, points 2411 may be detectedalong an edge of one or both of the continuous lines.

Vehicle 200 may also represent lane marks differently depending on thetype or shape of lane mark. FIG. 24B shows an exemplary dashed lane mark2420 that may be detected by vehicle 200. Rather than identifying edgepoints, as in FIG. 24A, vehicle may detect a series of corner points2421 representing corners of the lane dashes to define the full boundaryof the dash. While FIG. 24B shows each corner of a given dash markingbeing located, vehicle 200 may detect or upload a subset of the pointsshown in the figure. For example, vehicle 200 may detect the leadingedge or leading corner of a given dash mark, or may detect the twocorner points nearest the interior of the lane. Further, not every dashmark may be captured, for example, vehicle 200 may capture and/or recordpoints representing a sample of dash marks (e.g., every other, everythird, every fifth, etc.) or dash marks at a predefined spacing (e.g.,every meter, every five meters, every 10 meters, etc.) Corner points mayalso be detected for similar lane marks, such as markings showing a laneis for an exit ramp, that a particular lane is ending, or other variouslane marks that may have detectable corner points. Corner points mayalso be detected for lane marks consisting of double dashed lines or acombination of continuous and dashed lines.

In some embodiments, the points uploaded to the server to generate themapped lane marks may represent other points besides the detected edgepoints or corner points. FIG. 24C illustrates a series of points thatmay represent a centerline of a given lane mark. For example, continuouslane 2410 may be represented by centerline points 2441 along acenterline 2440 of the lane mark. In some embodiments, vehicle 200 maybe configured to detect these center points using various imagerecognition techniques, such as convolutional neural networks (CNN),scale-invariant feature transform (SIFT), histogram of orientedgradients (HOG) features, or other techniques. Alternatively, vehicle200 may detect other points, such as edge points 2411 shown in FIG. 24A,and may calculate centerline points 2441, for example, by detectingpoints along each edge and determining a midpoint between the edgepoints. Similarly, dashed lane mark 2420 may be represented bycenterline points 2451 along a centerline 2450 of the lane mark. Thecenterline points may be located at the edge of a dash, as shown in FIG.24C, or at various other locations along the centerline. For example,each dash may be represented by a single point in the geometric centerof the dash. The points may also be spaced at a predetermined intervalalong the centerline (e.g., every meter, 5 meters, 10 meters, etc.). Thecenterline points 2451 may be detected directly by vehicle 200, or maybe calculated based on other detected reference points, such as cornerpoints 2421, as shown in FIG. 24B. A centerline may also be used torepresent other lane mark types, such as a double line, using similartechniques as above.

In some embodiments, vehicle 200 may identify points representing otherfeatures, such as a vertex between two intersecting lane marks. FIG. 24Dshows exemplary points representing an intersection between two lanemarks 2460 and 2465. Vehicle 200 may calculate a vertex point 2466representing an intersection between the two lane marks. For example,one of lane marks 2460 or 2465 may represent a train crossing area orother crossing area in the road segment. While lane marks 2460 and 2465are shown as crossing each other perpendicularly, various otherconfigurations may be detected. For example, the lane marks 2460 and2465 may cross at other angles, or one or both of the lane marks mayterminate at the vertex point 2466. Similar techniques may also beapplied for intersections between dashed or other lane mark types. Inaddition to vertex point 2466, various other points 2467 may also bedetected, providing further information about the orientation of lanemarks 2460 and 2465.

Vehicle 200 may associate real-world coordinates with each detectedpoint of the lane mark. For example, location identifiers may begenerated, including coordinate for each point, to upload to a serverfor mapping the lane mark. The location identifiers may further includeother identifying information about the points, including whether thepoint represents a corner point, an edge point, center point, etc.Vehicle 200 may therefore be configured to determine a real-worldposition of each point based on analysis of the images. For example,vehicle 200 may detect other features in the image, such as the variouslandmarks described above, to locate the real-world position of the lanemarks. This may involve determining the location of the lane marks inthe image relative to the detected landmark or determining the positionof the vehicle based on the detected landmark and then determining adistance from the vehicle (or target trajectory of the vehicle) to thelane mark. When a landmark is not available, the location of the lanemark points may be determined relative to a position of the vehicledetermined based on dead reckoning. The real-world coordinates includedin the location identifiers may be represented as absolute coordinates(e.g., latitude/longitude coordinates), or may be relative to otherfeatures, such as based on a longitudinal position along a targettrajectory and a lateral distance from the target trajectory. Thelocation identifiers may then be uploaded to a server for generation ofthe mapped lane marks in the navigation model (such as sparse map 800).In some embodiments, the server may construct a spline representing thelane marks of a road segment. Alternatively, vehicle 200 may generatethe spline and upload it to the server to be recorded in thenavigational model.

FIG. 24E shows an exemplary navigation model or sparse map for acorresponding road segment that includes mapped lane marks. The sparsemap may include a target trajectory 2475 for a vehicle to follow along aroad segment. As described above, target trajectory 2475 may representan ideal path for a vehicle to take as it travels the corresponding roadsegment, or may be located elsewhere on the road (e.g., a centerline ofthe road, etc.). Target trajectory 2475 may be calculated in the variousmethods described above, for example, based on an aggregation (e.g., aweighted combination) of two or more reconstructed trajectories ofvehicles traversing the same road segment.

In some embodiments, the target trajectory may be generated equally forall vehicle types and for all road, vehicle, and/or environmentconditions. In other embodiments, however, various other factors orvariables may also be considered in generating the target trajectory. Adifferent target trajectory may be generated for different types ofvehicles (e.g., a private car, a light truck, and a full trailer). Forexample, a target trajectory with relatively tighter turning radii maybe generated for a small private car than a larger semi-trailer truck.In some embodiments, road, vehicle and environmental conditions may beconsidered as well. For example, a different target trajectory may begenerated for different road conditions (e.g., wet, snowy, icy, dry,etc.), vehicle conditions (e.g., tire condition or estimated tirecondition, brake condition or estimated brake condition, amount of fuelremaining, etc.) or environmental factors (e.g., time of day,visibility, weather, etc.). The target trajectory may also depend on oneor more aspects or features of a particular road segment (e.g., speedlimit, frequency and size of turns, grade, etc.). In some embodiments,various user settings may also be used to determine the targettrajectory, such as a set driving mode (e.g., desired drivingaggressiveness, economy mode, etc.).

The sparse map may also include mapped lane marks 2470 and 2480representing lane marks along the road segment. The mapped lane marksmay be represented by a plurality of location identifiers 2471 and 2481.As described above, the location identifiers may include locations inreal world coordinates of points associated with a detected lane mark.Similar to the target trajectory in the model, the lane marks may alsoinclude elevation data and may be represented as a curve inthree-dimensional space. For example, the curve may be a splineconnecting three dimensional polynomials of suitable order the curve maybe calculated based on the location identifiers. The mapped lane marksmay also include other information or metadata about the lane mark, suchas an identifier of the type of lane mark (e.g., between two lanes withthe same direction of travel, between two lanes of opposite direction oftravel, edge of a roadway, etc.) and/or other characteristics of thelane mark (e.g., continuous, dashed, single line, double line, yellow,white, etc.). In some embodiments, the mapped lane marks may becontinuously updated within the model, for example, using crowdsourcingtechniques. The same vehicle may upload location identifiers duringmultiple occasions of travelling the same road segment or data may beselected from a plurality of vehicles (such as 1205, 1210, 1215, 1220,and 1225) travelling the road segment at different times. Sparse map 800may then be updated or refined based on subsequent location identifiersreceived from the vehicles and stored in the system. As the mapped lanemarks are updated and refined, the updated road navigation model and/orsparse map may be distributed to a plurality of autonomous vehicles.

Generating the mapped lane marks in the sparse map may also includedetecting and/or mitigating errors based on anomalies in the images orin the actual lane marks themselves. FIG. 24F shows an exemplary anomaly2495 associated with detecting a lane mark 2490. Anomaly 2495 may appearin the image captured by vehicle 200, for example, from an objectobstructing the camera's view of the lane mark, debris on the lens, etc.In some instances, the anomaly may be due to the lane mark itself, whichmay be damaged or worn away, or partially covered, for example, by dirt,debris, water, snow or other materials on the road. Anomaly 2495 mayresult in an erroneous point 2491 being detected by vehicle 200. Sparsemap 800 may provide the correct the mapped lane mark and exclude theerror. In some embodiments, vehicle 200 may detect erroneous point 2491for example, by detecting anomaly 2495 in the image, or by identifyingthe error based on detected lane mark points before and after theanomaly. Based on detecting the anomaly, the vehicle may omit point 2491or may adjust it to be in line with other detected points. In otherembodiments, the error may be corrected after the point has beenuploaded, for example, by determining the point is outside of anexpected threshold based on other points uploaded during the same trip,or based on an aggregation of data from previous trips along the sameroad segment.

The mapped lane marks in the navigation model and/or sparse map may alsobe used for navigation by an autonomous vehicle traversing thecorresponding roadway. For example, a vehicle navigating along a targettrajectory may periodically use the mapped lane marks in the sparse mapto align itself with the target trajectory. As mentioned above, betweenlandmarks the vehicle may navigate based on dead reckoning in which thevehicle uses sensors to determine its ego motion and estimate itsposition relative to the target trajectory. Errors may accumulate overtime and vehicle's position determinations relative to the targettrajectory may become increasingly less accurate. Accordingly, thevehicle may use lane marks occurring in sparse map 800 (and their knownlocations) to reduce the dead reckoning-induced errors in positiondetermination. In this way, the identified lane marks included in sparsemap 800 may serve as navigational anchors from which an accurateposition of the vehicle relative to a target trajectory may bedetermined.

FIG. 25A shows an exemplary image 2500 of a vehicle's surroundingenvironment that may be used for navigation based on the mapped lanemarks. Image 2500 may be captured, for example, by vehicle 200 throughimage capture devices 122 and 124 included in image acquisition unit120. Image 2500 may include an image of at least one lane mark 2510, asshown in FIG. 25A. Image 2500 may also include one or more landmarks2521, such as road sign, used for navigation as described above. Someelements shown in FIG. 25A, such as elements 2511, 2530, and 2520 whichdo not appear in the captured image 2500 but are detected and/ordetermined by vehicle 200 are also shown for reference.

Using the various techniques described above with respect to FIGS. 24A-Dand 24F, a vehicle may analyze image 2500 to identify lane mark 2510.Various points 2511 may be detected corresponding to features of thelane mark in the image. Points 2511, for example, may correspond to anedge of the lane mark, a corner of the lane mark, a midpoint of the lanemark, a vertex between two intersecting lane marks, or various otherfeatures or locations. Points 2511 may be detected to correspond to alocation of points stored in a navigation model received from a server.For example, if a sparse map is received containing points thatrepresent a centerline of a mapped lane mark, points 2511 may also bedetected based on a centerline of lane mark 2510.

The vehicle may also determine a longitudinal position represented byelement 2520 and located along a target trajectory. Longitudinalposition 2520 may be determined from image 2500, for example, bydetecting landmark 2521 within image 2500 and comparing a measuredlocation to a known landmark location stored in the road model or sparsemap 800. The location of the vehicle along a target trajectory may thenbe determined based on the distance to the landmark and the landmark'sknown location. The longitudinal position 2520 may also be determinedfrom images other than those used to determine the position of a lanemark. For example, longitudinal position 2520 may be determined bydetecting landmarks in images from other cameras within imageacquisition unit 120 taken simultaneously or near simultaneously toimage 2500. In some instances, the vehicle may not be near any landmarksor other reference points for determining longitudinal position 2520. Insuch instances, the vehicle may be navigating based on dead reckoningand thus may use sensors to determine its ego motion and estimate alongitudinal position 2520 relative to the target trajectory. Thevehicle may also determine a distance 2530 representing the actualdistance between the vehicle and lane mark 2510 observed in the capturedimage(s). The camera angle, the speed of the vehicle, the width of thevehicle, or various other factors may be accounted for in determiningdistance 2530.

FIG. 25B illustrates a lateral localization correction of the vehiclebased on the mapped lane marks in a road navigation model. As describedabove, vehicle 200 may determine a distance 2530 between vehicle 200 anda lane mark 2510 using one or more images captured by vehicle 200.Vehicle 200 may also have access to a road navigation model, such assparse map 800, which may include a mapped lane mark 2550 and a targettrajectory 2555. Mapped lane mark 2550 may be modeled using thetechniques described above, for example using crowdsourced locationidentifiers captured by a plurality of vehicles. Target trajectory 2555may also be generated using the various techniques described previously.Vehicle 200 may also determine or estimate a longitudinal position 2520along target trajectory 2555 as described above with respect to FIG.25A. Vehicle 200 may then determine an expected distance 2540 based on alateral distance between target trajectory 2555 and mapped lane mark2550 corresponding to longitudinal position 2520. The laterallocalization of vehicle 200 may be corrected or adjusted by comparingthe actual distance 2530, measured using the captured image(s), with theexpected distance 2540 from the model.

FIG. 26A is a flowchart showing an exemplary process 2600A for mapping alane mark for use in autonomous vehicle navigation, consistent withdisclosed embodiments. At step 2610, process 2600A may include receivingtwo or more location identifiers associated with a detected lane mark.For example, step 2610 may be performed by server 1230 or one or moreprocessors associated with the server. The location identifiers mayinclude locations in real-world coordinates of points associated withthe detected lane mark, as described above with respect to FIG. 24E. Insome embodiments, the location identifiers may also contain other data,such as additional information about the road segment or the lane mark.Additional data may also be received during step 2610, such asaccelerometer data, speed data, landmarks data, road geometry or profiledata, vehicle positioning data, ego motion data, or various other formsof data described above. The location identifiers may be generated by avehicle, such as vehicles 1205, 1210, 1215, 1220, and 1225, based onimages captured by the vehicle. For example, the identifiers may bedetermined based on acquisition, from a camera associated with a hostvehicle, of at least one image representative of an environment of thehost vehicle, analysis of the at least one image to detect the lane markin the environment of the host vehicle, and analysis of the at least oneimage to determine a position of the detected lane mark relative to alocation associated with the host vehicle. As described above, the lanemark may include a variety of different marking types, and the locationidentifiers may correspond to a variety of points relative to the lanemark. For example, where the detected lane mark is part of a dashed linemarking a lane boundary, the points may correspond to detected cornersof the lane mark. Where the detected lane mark is part of a continuousline marking a lane boundary, the points may correspond to a detectededge of the lane mark, with various spacings as described above. In someembodiments, the points may correspond to the centerline of the detectedlane mark, as shown in FIG. 24C, or may correspond to a vertex betweentwo intersecting lane marks and at least one two other points associatedwith the intersecting lane marks, as shown in FIG. 24D.

At step 2612, process 2600A may include associating the detected lanemark with a corresponding road segment. For example, server 1230 mayanalyze the real-world coordinates, or other information received duringstep 2610, and compare the coordinates or other information to locationinformation stored in an autonomous vehicle road navigation model.Server 1230 may determine a road segment in the model that correspondsto the real-world road segment where the lane mark was detected.

At step 2614, process 2600A may include updating an autonomous vehicleroad navigation model relative to the corresponding road segment basedon the two or more location identifiers associated with the detectedlane mark. For example, the autonomous road navigation model may besparse map 800, and server 1230 may update the sparse map to include oradjust a mapped lane mark in the model. Server 1230 may update the modelbased on the various methods or processes described above with respectto FIG. 24E. In some embodiments, updating the autonomous vehicle roadnavigation model may include storing one or more indicators of positionin real world coordinates of the detected lane mark. The autonomousvehicle road navigation model may also include a at least one targettrajectory for a vehicle to follow along the corresponding road segment,as shown in FIG. 24E.

At step 2616, process 2600A may include distributing the updatedautonomous vehicle road navigation model to a plurality of autonomousvehicles. For example, server 1230 may distribute the updated autonomousvehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and1225, which may use the model for navigation. The autonomous vehicleroad navigation model may be distributed via one or more networks (e.g.,over a cellular network and/or the Internet, etc.), through wirelesscommunication paths 1235, as shown in FIG. 12 .

In some embodiments, the lane marks may be mapped using data receivedfrom a plurality of vehicles, such as through a crowdsourcing technique,as described above with respect to FIG. 24E. For example, process 2600Amay include receiving a first communication from a first host vehicle,including location identifiers associated with a detected lane mark, andreceiving a second communication from a second host vehicle, includingadditional location identifiers associated with the detected lane mark.For example, the second communication may be received from a subsequentvehicle travelling on the same road segment, or from the same vehicle ona subsequent trip along the same road segment. Process 2600A may furtherinclude refining a determination of at least one position associatedwith the detected lane mark based on the location identifiers receivedin the first communication and based on the additional locationidentifiers received in the second communication. This may include usingan average of the multiple location identifiers and/or filtering out“ghost” identifiers that may not reflect the real-world position of thelane mark.

FIG. 26B is a flowchart showing an exemplary process 2600B forautonomously navigating a host vehicle along a road segment using mappedlane marks. Process 2600B may be performed, for example, by processingunit 110 of autonomous vehicle 200. At step 2620, process 2600B mayinclude receiving from a server-based system an autonomous vehicle roadnavigation model. In some embodiments, the autonomous vehicle roadnavigation model may include a target trajectory for the host vehiclealong the road segment and location identifiers associated with one ormore lane marks associated with the road segment. For example, vehicle200 may receive sparse map 800 or another road navigation modeldeveloped using process 2600A. In some embodiments, the targettrajectory may be represented as a three-dimensional spline, forexample, as shown in FIG. 9B. As described above with respect to FIGS.24A-F, the location identifiers may include locations in real worldcoordinates of points associated with the lane mark (e.g., corner pointsof a dashed lane mark, edge points of a continuous lane mark, a vertexbetween two intersecting lane marks and other points associated with theintersecting lane marks, a centerline associated with the lane mark,etc.).

At step 2621, process 2600B may include receiving at least one imagerepresentative of an environment of the vehicle. The image may bereceived from an image capture device of the vehicle, such as throughimage capture devices 122 and 124 included in image acquisition unit120. The image may include an image of one or more lane marks, similarto image 2500 described above.

At step 2622, process 2600B may include determining a longitudinalposition of the host vehicle along the target trajectory. As describedabove with respect to FIG. 25A, this may be based on other informationin the captured image (e.g., landmarks, etc.) or by dead reckoning ofthe vehicle between detected landmarks.

At step 2623, process 2600B may include determining an expected lateraldistance to the lane mark based on the determined longitudinal positionof the host vehicle along the target trajectory and based on the two ormore location identifiers associated with the at least one lane mark.For example, vehicle 200 may use sparse map 800 to determine an expectedlateral distance to the lane mark. As shown in FIG. 25B, longitudinalposition 2520 along a target trajectory 2555 may be determined in step2622. Using spare map 800, vehicle 200 may determine an expecteddistance 2540 to mapped lane mark 2550 corresponding to longitudinalposition 2520.

At step 2624, process 2600B may include analyzing the at least one imageto identify the at least one lane mark. Vehicle 200, for example, mayuse various image recognition techniques or algorithms to identify thelane mark within the image, as described above. For example, lane mark2510 may be detected through image analysis of image 2500, as shown inFIG. 25A.

At step 2625, process 2600B may include determining an actual lateraldistance to the at least one lane mark based on analysis of the at leastone image. For example, the vehicle may determine a distance 2530, asshown in FIG. 25A, representing the actual distance between the vehicleand lane mark 2510. The camera angle, the speed of the vehicle, thewidth of the vehicle, the position of the camera relative to thevehicle, or various other factors may be accounted for in determiningdistance 2530.

At step 2626, process 2600B may include determining an autonomoussteering action for the host vehicle based on a difference between theexpected lateral distance to the at least one lane mark and thedetermined actual lateral distance to the at least one lane mark. Forexample, as described above with respect to FIG. 25B, vehicle 200 maycompare actual distance 2530 with an expected distance 2540. Thedifference between the actual and expected distance may indicate anerror (and its magnitude) between the vehicle's actual position and thetarget trajectory to be followed by the vehicle. Accordingly, thevehicle may determine an autonomous steering action or other autonomousaction based on the difference. For example, if actual distance 2530 isless than expected distance 2540, as shown in FIG. 25B, the vehicle maydetermine an autonomous steering action to direct the vehicle left, awayfrom lane mark 2510. Thus, the vehicle's position relative to the targettrajectory may be corrected. Process 2600B may be used, for example, toimprove navigation of the vehicle between landmarks

Navigation Based on Simulated Drone View

As described above, the disclosed systems and methods may includecollecting information from vehicles or other sources and generating asparse map for vehicle navigation based on the collected information.Such information may include images collected by the vehicles, forexample, using onboard cameras of the vehicles. The images captured bythe vehicles may be analyzed to identify features within the environmentof the vehicle, which may be used to generate the sparse map. In someembodiments, the images may be processed to allow more accurate and/orefficient detection of features within the images. Such processing mayinclude warping an image to simulate a different view or perspectivethan that of the camera used to capture the image. For example, theimage may be warped to simulate a “drone view,” which may be elevatedwith respect to the actual camera used to capture the image. Thissimulated view may allow for improved detection of lane marks and otherfeatures within the images, especially at greater distances. Forexample, road features in the warped view may appear larger as comparedto the original image, which may make them easier to detect. Further,the warped view may normalize geometries of lane marks so that they aremore easily detectable. For example, the geometries of the lane marks orother road features may be detected with a greater confidence level thanlane marks detected in the captured images. In other embodiments, thelane marks or other features may be detected more quickly and/orefficiently (e.g., requiring less computing resources) than if the samefeatures were detected in the captured images.

In the disclosed systems and methods, therefore, a central server may beconfigured to receive images from one or more vehicles, which mayinclude autonomous vehicles. For example, vehicle 200 (or vehicles 1205,1210, 1215, 1220, or 1225) may upload the images and other data toserver 1230 using the various wireless communication methods discussedabove. The images may be captured by image sensors onboard the vehicles.For example, the images may be captured using image acquisition unit 120(e.g., through image capture device 122) of vehicle 200, as describedabove. The image sensors may include any suitable type of image capturedevice, such as an Aptina M9V024 WVGA sensor with a global shutter, asdescribed above, or any other form of image sensor. The simulated droneview may then be generated based on the images captured by the imagesensors. In some embodiments, the simulated drone view may be generatedby the server after receiving the images from the vehicles. In otherembodiments, the simulated drone view may be generated by a processingdevice on the vehicle and a warped image may be transmitted to theserver. Accordingly, the server may receive a plurality of warped imagesfrom one or more vehicles, which may be used to generate the sparse map.

FIG. 27 is a diagrammatic side view representation of an example vehicle2700 for use in generating a simulated drone view consistent withdisclosed embodiments. Vehicle 2700 may be the same as or similar tovehicle 200 described herein. Accordingly, any of the descriptions ordisclosures made herein in reference to vehicle 200 may also apply tovehicle 2700. Vehicle 2700 may include an image capture device 2720 forcapturing images within the environment of vehicle 2700. Image capturedevice 2720 may correspond to image capture devices 122, 124, and/or126, as described above. Image capture device 2720 may be positioned onvehicle 2700 in any position suitable for capturing images within theenvironment of vehicle 2700, including on a bumper, on the roof, behindthe windshield, on a hood, or in any other suitable location of vehicle2700.

Vehicle 2700 may further include a processing unit 2710 configured toprocess images captured by image capture device 2720 and transmit themto a server, such as server 1230, for generating a sparse map.Processing unit 2710 may comprise various types of devices. For example,processing unit 2710 may include a controller, an image preprocessor, acentral processing unit (CPU), a graphics processing unit (GPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The GPU may also compriseany number of microcontrollers or microprocessors. In some embodiments,processing unit 2710 may correspond to processing unit 110, described indetail above. Vehicle 2700 may include a wireless transceiver (not shownin FIG. 27 ), such as wireless transceiver 172 described above, fortransmitting images and other information to the central server.

Consistent with the disclosed embodiments, a warped image may begenerated based on the captured images. As used herein, a “warped image”may refer to an image that has been digitally manipulated to distort thelocation of features appearing within the image. For example, positionsof various features in an original image may be remapped to appear indifferent locations in the warped image. The generated warped image maysimulate a view of features in the environment of vehicle 2700 from asimulated viewpoint elevated relative to an actual position of thecamera. For example, the warped image may simulate an image taken from aviewpoint 2730 located above image capture device 2720. Images warpedbased on viewpoint 2730 may simulate images taken by an imaginary cameraat that location, for example, a camera on a drone or other elevatedobject (as illustrated by the dotted outline of a drone in FIG. 27 ).Accordingly, viewpoint 2730 may also be referred to herein as a“simulated drone view.” It is to be understood, however, that simulatedviewpoint 2730 is purely hypothetical and no images are actuallycaptured from an elevated location. Rather, the original images arecaptured by one or more image capture devices 2720 of vehicle 2700 andare warped to simulate viewpoint 2730. The warped image may be generatedby a processor of a server, such as server 1230, or may be generated byvehicle 2700, for example, using processing unit 2710.

Simulated viewpoint 2730 may simulate various heights above camera 2720.In some embodiments, simulated viewpoint 2730 may be elevated by onemeter, two meters, five meters, ten meters, 15 meters, 20 meters, or anyother height relative to camera 2720 that may be simulated based onimage warping. In some embodiments, the warped image may be based on asimulated camera optical axis that is angled downward relative to anactual optical axis of the camera. As used herein an “optical axis” mayrefer to an imaginary line along which there is some degree ofrotational symmetry in an optical system. The optical axis may representan imaginary line passing through the center of curvature of a lens. Forexample, image capture device 2720 may have an optical axis 2722, asshown in FIG. 27 . Simulated viewpoint 2730 may have a simulated opticalaxis 2732 that is rotated with respect to optical axis 2722. Simulatedoptical axis 2732 may be angled downward relative to optical axis 2722at various angles, including 25 degrees, 35 degrees, 45 degrees, 55degrees, or any other angle between 0 and 90 degrees. The warped imagemay be generated to simulate how an image taken at simulated viewpoint2730 with a simulated optical axis 2722. Accordingly, the degree andtype of image warping that is performed on the images may depend on thesimulated height of viewpoint 2730 and the simulated optical axis 2732.

The warped image may be generated using various transformationalgorithms and techniques. For example, pixels from the original imagemay be translated, scaled, and/or rotated to form the warped image usingone or more transformation algorithms. Such transformations may includea Procrustes transformation, affine transformation, perspectivetransformation, bilinear transformation, polynomial transformation, anelastic deformation, a thin-plate spline technique, a Bayesian approach,a mesh-warping technique, or any other transformation. In someembodiments, a combination of one or more transformation techniques maybe used. FIGS. 28A and 28B illustrate an example image transformationconsistent with disclosed embodiments. As shown in FIG. 28A, an originalimage 2810 may include various pixels 2802, 2804, and 2806. For example,the pixels may correspond to features in the environment of vehicle2700, such as edges or points of features represented in the image. Forpurposes of illustration, original image 2810 may be associated with acoordinate system expressed in terms of x- and y-coordinates. Forexample, pixel 2802 may have a position of (x,y), as shown.

Original image 2810 may be transformed to generate a warped image 2820,as shown in FIG. 28B. Original image 2810 is overlaid on warped image2820 for reference. The various points from original image may betransformed to form warped image 2820. For example, pixel 2802 may havea new position in warped image 2820 of (x′,y′), which may besignificantly different than the original position (x,y), as shown inFIG. 28B. The shape of original image 2810 relative to warped image 2820is provided by way of example. Various other types or degrees oftransformation may be implemented to imitate simulated viewpoint 2730and simulated optical axis 2732.

In some embodiments, the simulated drone view may allow features withinthe environment of vehicle 2700 to be detected more easily oraccurately. In some embodiments, this may be due to a feature taking upmore space in the warped image as compared to the original image due tothe transformation that is performed. For example, objects or featuresin the distance may be warped to appear larger in the warped image,similar to how they might appear if captured by a camera on a drone. Forexample, as shown in FIG. 28A, original image 2810 may include pixels2804 and 2806, which may correspond to a feature in the environment ofvehicle 2700. When the original image 2810 is transformed to generatewarped image 2820 as shown in FIG. 28B, pixels 2804 and 2806 may bespaced further apart from each other relative to the original image.Accordingly, the feature represented by these pixels may appear largerin the warped image and thus may be more likely to be detected.

In some embodiments, a representation of a feature in the warped imagemay include more image pixels than the representation of the feature inthe original captured image, which may make the feature easier todetect. For example, as discussed above, pixels 2804 and 2806 may bespaced further apart in warped image 2820 than in original image 2810.In some embodiments, as part of generating warped image 2820, additionalpixels, such as pixel 2805, may be added to account for the increasedspacing between pixels 2804 and 2806. Therefore, a feature representedby pixels 2804 and 2806 may have an increased resolution in the warpedimage. Various algorithms may be used to generate pixel 2805, including,for example, upscaling algorithms configured to increase pixelresolution in an image. For example, such algorithms may includenearest-neighbor interpolation, bilinear interpolation, bicubicinterpolation, sinc or lanczos resampling, box sampling,fourier-transform methods, edge-directed interpolation, high qualityscale (“hqx”), seam carving, and/or vector extraction. In someembodiments, machine learning models, including models based on deepconvolutional neural networks (such as waifu2x, Neural Enhance, TopazA.I. Gigapixel, etc.) may be used to increase the image resolution. Suchalgorithms may be applied to the entire warped image or may be appliedto certain regions depending on the degree and type of transformationperformed in that region. Various noise reduction algorithms may also beapplied to smooth the upscaled image or portions of the image.Accordingly, features represented at a higher resolution in the warpedimage may be easier to detect and thus the accuracy or efficiency ingenerating sparse maps may be improved.

In some aspects of the disclosed embodiments, at least one road featurerepresented in the warped image may be identified. The representation ofthe road feature may be transformed in one or more respects relative tothe representation of the road feature in the original captured image,as discussed above. FIGS. 29A and 29B are example images that may beused as part of generating a simulated drone view consistent withdisclosed embodiments. FIG. 29A is an illustration of an examplecaptured image 2910 that may be used to generate a warped image. Forexample, captured image 2910 may correspond to original image 2810,discussed above. Captured image 2910 may be an image captured by animage capture device, such as image capture device 2720. Accordingly,captured image 2910 may include representations of one or more featuresfrom within the environment of vehicle 2700, including other vehicles inthe environment of vehicle 2700, such as vehicle 2900. In someembodiments, these features may include road features, such asrecognized landmarks, road signature profiles, or any other road-relatedfeatures useful in navigating a vehicle. In some embodiments, the roadfeature may include a lane mark. A lane mark may include any marking forguiding vehicle traffic on a roadway. For example, a lane marks may becontinuous or dashed lines demarking the edge of a lane of travel, suchas lane marks 2912 and 2914, as shown in FIG. 29A. In some embodiments,the lane mark may indicate a lane split, such as a merging lane, an exitlane, a turn lane, or similar lane splits. In some embodiments, the roadfeatures may include a roundabout or other intersection within aroadway. The road features may also represent a path or direction of theroadway, such as a curve in the road, as shown in FIG. 29A.

FIG. 29B is an illustration of an example warped image 2920 that may begenerated based on captured image 2910. Warped image 2920 may correspondto warped image 2810 and may be generated using any of the techniquesdescribed above in reference to FIGS. 28A and 28B. Warped image 2920 maybe generated to simulate an image as it would appear if it were capturedat simulated viewpoint 2730 and with a simulated optical axis 2732.Because it is a simulated image, it may differ in some aspects from animage that was actually captured by a drone at simulated view 2730. Forexample, warped image 2920 may be warped such that warped imagelocations would match an actual drone image for points on the planewhich defines the warp homography (e.g., the road surface). In otherwords, points on the road surface will appear in the correct locationsin warped image 2920 compared to an actual camera image captured fromthe drone view. A point located on an object above this plane, however,will be warped to an incorrect location that is further out and higherin the image than if that point was captured from an actual dronecamera. An example of this can be seen in FIG. 29B where vehicle 2900appears to be leaning outward. The contact point of the wheels ofvehicle 2900 with the road plane, however, would be consistent with anactual drone image. Because warped image 2920 is generated from capturedimage 2910, it may not include information about the environment ofvehicle 2700 that is not present in captured image 2910. For example,warped image would not show the top of vehicle 2900 or show what isbehind vehicle 2900 in the environment of vehicle 2700. Conversely, inan image captured from a real drone flying in the environment of vehicle2700, the top of vehicle 2900 would appear lower in the image and moreof the scene beyond vehicle 2900 would be visible.

Warped image 2920 may be transformed to simulate an image captured atsimulated view 2730. For example, lane mark 2912 may be normalized suchthat it appears to be captured from above, as shown in FIG. 29B. Thismay allow the curvature of the lane markings to be identified moreaccurately using the various systems and methods for detecting lanemarks described above. For example, a path of lane mark 2912 along aroadway may be traced more accurately or efficiently since theappearance of the lane mark in warped image 2920 may more closelyapproximate how it would appear in an aerial view.

Further, as described above in reference to FIG. 28B, more distantfeatures in the environment of vehicle 2700 may be detected more easilyor accurately. For example, lane mark 2914 may appear larger in warpedimage 2920 as compared to captured image 2910, and thus may be morelikely to be detected using the various techniques for lane markdetection as described above. Further, lane mark 2914 may be displayedat a higher resolution in warped image 2920 as compared to 2910 and thusmay be easier to detect. Therefore, road features at greater distancesfrom image capture device 2720 (e.g., 50 meters, 60 meters, 70 meters,100 meters, 150 meters, etc.) may be more likely to be detected.Similarly, lower resolution cameras may be used to detect features atthese distances. For example, image capture device 2720 may have animage resolution of around 300 pixels per inch or less and may be ableto detect road features at the distances listed above based on warpedimage 2920. In other examples, image capture device 2720 may have otherimage resolutions (e.g., 500 pixels per inch or less, 400 pixels perinch or less, 200 pixels per inch or less, 100 pixels per inch or less,etc.).

In some embodiments, warped image 2920 may be warped relative to aground plane associated with a road surface. For example, lane marks2912 and 2914 may be determined to appear along a ground plane incaptured image 2910. Warped image 2920 may be generated by warpingpixels corresponding to the ground plane to align with a second warpedground plane, as shown in FIG. 29B. Such warping may correspond to thetransformation techniques described above with respect to FIGS. 28A and28B.

In some embodiments, a navigational action for vehicle 2700 may bedetermined based on road features identified in the warped image. Forexample, the navigational action may include a braking action, acoasting action, a steering action, or the like, or a combinationthereof, as described above with respect to navigation based on mappedlane marks. The disclosed systems and methods may further includecausing at least one actuator system of the vehicle to implement thedetermined navigational action. For example, based on the determinedautonomous navigational action, vehicle 2700 may be configured tooperate one or more components of the vehicle, such as a steeringmechanism, braking mechanism, or various other components.

FIG. 30 is a flowchart showing an example process 3000 for mapping aroad feature for use in vehicle navigation consistent with disclosedembodiments. Process 3000 may be performed, for example, by processingunit 2710 of vehicle 2700. In other embodiments, some or all of process3000 may be performed by a server, such as server 1230. In someembodiments, a non-transitory computer readable medium may containinstructions that when executed by a processor cause the processor toperform process 3000.

At step 3010, process 3000 may include receiving, from a camera, atleast one captured image representative of features in an environment ofthe vehicle. For example, step 3010 may include receiving an image, suchas captured image 2910, which may have been captured by image capturedevice 2720 of vehicle 2700. The captured image may therefore includerepresentations of features within the environment of vehicle 2700, suchas lane markings 2912 and 2914. In some embodiments, the vehicle may bean autonomous vehicle, as described above.

At step 3020, process 3000 may include generating a warped image basedon the received at least one captured image. For example, warped image2920 may be generated based on captured image 2920 using the imagewarping techniques described above. The warped image may simulate a viewof one or more of the features in the environment of the vehicle from asimulated viewpoint elevated relative to an actual position of thecamera. For example, if the captured image is captured using imagecapture device 2720, the warped image may simulate viewpoint 2730, asdescribed above with respect to FIG. 27 .

In some embodiments, the simulated viewpoint may be elevated by at leastten meters relative to the actual position of the camera. In particular,the simulated viewpoint may be elevated by between ten meters and twentymeters relative to the actual position of the camera. In someembodiments, the warped image may further simulate a view of the one ormore features in the environment of the vehicle based on a simulatedcamera optical axis. In some embodiments, the simulated optical axis maybe angled downward relative to an actual optical axis of the camera. Forexample, the warped image may be based on simulated optical axis 2732,which may be angled downward relative to optical axis 2722 of imagecapture device 2720. In some embodiments, the simulated camera opticalaxis may be downwardly angled relative to the actual optical axis of thecamera by an angle of between thirty-five degrees and fifty-fivedegrees. Various other angles may also be used, as described above. Thedegree and type of warping that is applied to the original image maydepend on the height of the simulated viewpoint and/or the angle of thesimulated optical axis. Further, in some embodiments, the warped imagemay be warped relative to a ground plane associated with a road surface,as discussed above.

At step 3030, process 3000 may include identifying at least one roadfeature represented in the warped image. The at least one road featuremay correspond to any of the example road features described throughoutthe present disclosure. In some embodiments, for example, the identifiedat least one road feature may include a lane mark, such as lane mark2912 and/or 2914. For example, the at least one road feature may includeone or more lane marks indicating a lane split. In some embodiments, theidentified at least one road feature may include a roundabout. In otherembodiments, the identified at least one road feature may include acurve in the road. The representation of the at least one road featurein the warped image may be transformed in one or more respects relativeto a representation of the at least one road feature in the at least onecaptured image. For example, the at least one road feature in the warpedimage may be translated, scaled, and/or rotated in the warped image ascompared to the captured image. In some embodiments, the representationof the at least one road feature in the warped image may include moreimage pixels than the representation of the at least one road feature inthe at least one captured image. Accordingly, one or more algorithms maybe used to determine additional pixels to include in the warped image,for example, using the image upscaling algorithms described in greaterdetail above. In some embodiments, the warped image may allow forobjects to be detected at further distances at the same cameraresolution as compared to the original images. For example, theidentified road feature may be located at least sixty meters ahead ofthe actual camera position and the camera may have a resolution of nomore than three hundred pixels per inch.

At step 3040, process 3000 may include determining a navigational actionfor the vehicle based on the identified at least one road featurerepresented in the warped image. In some embodiments, the navigationalaction may include at least one of braking or a change in headingdirection. For example, based on a lane mark detected in the warpedimage, vehicle 2700 may perform a steering maneuver to maintain aposition within a lane. In some embodiments, the navigational action mayinclude an acceleration or deceleration, maintaining a current speed,maintaining a current heading direction, performing a merge maneuver, orvarious other navigational actions.

At step 3050, process 3000 may include causing at least one actuatorsystem of the vehicle to implement the determined navigational action.Consistent with the disclosed embodiments, the at least one actuatorsystem may include at least one of a steering mechanism, brakingmechanism, or various other components capable of performing anavigational action. In embodiments, where process 3000 is performed byprocessing unit 2710, step 3050 may include communicating directly withan actuator system within vehicle 2700. On the other hand, inembodiments where process 3000 is performed on an external server, step3050 may include transmitting instructions to a processing unit of avehicle (such as processing unit 2710) to cause the at least oneactuator system of the vehicle to implement the determined navigationalaction.

In some embodiments, one or more pixels of a road surface may beanalyzed to identify lane marks or other road features with sub-pixelaccuracy. For example, each pixel may be analyzed to determine if itrepresents a road feature, such as a painted lane mark on the surface ofa road. Using a trained model, or other algorithm, an estimated startpoint of the lane mark and end point of the lane mark may be identified.For example, for each pixel representing a dash along a dashed linebetween lanes, an estimated start point of the dash and end point of thedash may be identified. This process may be repeated for each pixelassociated with the lane mark. As a result, a cloud of pointsrepresenting the start point of the lane mark, and a cloud of pointsrepresenting the end of the lane mark may be determined. These clouds ofpoints may then be averaged to provide a start and end location of thelane mark with sub-pixel precision. This may be particularly effectivewhen used in combination with the image warping techniques describedabove. For example, using a drone view transformation of images, roadfeatures at greater distances may be identified at greater precisionusing the point cloud averaging techniques. While described herein inreference to a warped image, it is understood that the sub-pixel lanemark start and endpoint detection may be performed on non-warped images,such as images captured by image capture device 2720.

FIGS. 31A, 31B, and 31C illustrate an example process for detectingstart and end points of a lane mark consistent with disclosedembodiments. FIG. 31A shows a region 3100 of an image, such as warpedimage 2920 shown in FIG. 29B. Region 3100 may be a small portion of theoverall image and may include an array of pixels, as shown. Region 3100may include a representation of a lane mark 3110 that may be detectedusing the disclosed systems and methods. Lane mark 3110 may be locatedalong a path 3150 which may be included in a sparse map, as disclosedabove. In some embodiments, lane mark 3110 may be a lane mark at adistance (e.g., 60 meters or more) from the device that captured theimage, such as lane mark 2914. Accordingly, the lane mark may berepresented by a relatively small number of pixels. For example, asshown in FIG. 31A, lane mark 3110 may be represented by only 10 pixels,which may include pixels 3102 and 3104. This pixel density is used forexample only and the disclosed techniques may be used for any number ofpixels representing a road feature. Further, the present disclosure isnot limited to lane marks or other features at any particular distanceor range of distances from the image capture device. For example, lanemark 3110 may be a closer lane mark, such as lane mark 2912. While lanemark 3110 is used in FIGS. 31A-31C by way of example, the disclosedtechniques may be similarly used for other road features.

Using the disclosed systems and methods, a start point and an end pointfor lane mark 3110 may be estimated. For each pixel of the warped imagethat overlaps a portion of the at least one road feature, a first pointlocation associated with a leading edge of the lane mark and a secondpoint location associated with a trailing edge of the lane mark may bepredicted. FIG. 31B shows example point locations 3122 and 3124 that maybe predicted consistent with the disclosed embodiments. For example,based on pixel 3102, end position 3122 and start position 3132 of lanemark 3110 may be estimated. Similarly, end position 3124 and startposition 3134 of lane mark 3110 may be estimated based on pixel 3104. Insome embodiments, the start and end positions may be expressed relativeto the image, for example, using coordinates relative to the image. Inother embodiments, the start and end positions may be expressed relativeto the road feature, for example in real-world coordinates (e.g.,latitude and longitude coordinates).

The predicted start and end positions of a lane mark may be determinedfor each pixel using a variety of methods. In some embodiments, thestart and end positions may be determined based on a color and/ordarkness of the pixel. For example, if the lane mark is a white lanemark painted on a black road surface, each pixel may represent varyingshades of gray depending on what portion of the pixel is including thelane mark as compared to the portion of the pixel including the blackroad surface. For example, pixel 3104 may be relatively white ascompared to pixel 3102, which may be some shade of gray. Based on thecolor of the pixel, a start and end point of the lane mark may bepredicted. In some embodiments, the start and end point may bedetermined based on the pixels position relative to other pixelsincluding the lane mark. If the lane mark is represented in 10 pixels inregion 3100, as shown in FIG. 31A, the relative position of pixel 3102among the 10 pixels may be used to estimate the start and end positionof lane mark 3110 for that pixel. In some embodiments this may becombined with the brightness/color analysis described above. In someembodiments, a trained machine learning model may be used to predict thestart and end points of lane mark 3110. For example, data associatedwith pixels including a lane mark may be provided to a machine learningmodel along with known start and end point locations of the associatedlane mark. Based on this training data, a trained model may bedeveloped, which may be used for predicting start and end points ofother lane marks represented in images based on the corresponding pixeldata. Various machine learning algorithms may be used for developing thetrained model, including a logistic regression, a linear regression, aregression, a random forest, a K-Nearest Neighbor (KNN) model, a K-Meansmodel, a decision tree, a cox proportional hazards regression model, aNaïve Bayes model, a Support Vector Machines (SVM) model, a gradientboosting algorithm, an artificial neural network, or any other form ofmachine learning model or algorithm.

This process may be repeated for all pixels associated with lane mark3110 and the predicted points may be clustered. FIG. 31C shows exampleclusters 3142 (corresponding to an end position of lane mark 3110) and3144 (corresponding to a start position of lane mark 3110) that may bedeveloped after this process has been performed for each of the pixels.The points in each of clusters 3142 and 3144 may then be aggregated todetermine respective point positions. For example, an aggregatedtrailing edge point position may be determined based on aggregatingpoints of cluster 3142 and an aggregated leading edge may be determinedbased on aggregating points of cluster 3144. Accordingly, the determinedaggregated leading edge point position and the determined aggregatedtrailing edge point position may have sub-pixel accuracy relative to aresolution of the warped image. These determined positions of the startand end points of the lane mark may then be forwarded to a server foruse in updating a road navigation model, such as a sparse map, asdescribed above.

Aggregating the points may include any method for combining the pointswithin the cluster to determine a single point. For example, aggregatingthe points may include determining an average. Each horizontal pointposition and each vertical point position relative to the image may beaveraged to determine an averaged horizontal and vertical position,which may represent the aggregated point. In some embodiments. Aweighted average may be determined, for example, based on a relativeconfidence associated with each point. For example, point locationsdetermined based on pixels close to the leading edge may have a higherexpected accuracy (and thus a higher confidence rating) than pointlocations determined based on pixels further from the leading edge inthe image. Various other averaging or statistical analysis techniquesmay be performed to determine the aggregated leading and trailing edgepositions.

FIG. 32 is a flowchart showing an example process 3200 for mapping aroad feature for use in vehicle navigation consistent with disclosedembodiments. Similar to process 3000, process 3200 may be performed, forexample, by processing unit 2710 of vehicle 2700. In other embodiments,some or all of process 3200 may be performed by a server, such as server1230. In some embodiments, a non-transitory computer readable medium maycontain instructions that when executed by a processor cause theprocessor to perform process 3200.

At step 3210, process 3200 may include receiving, from a camera, atleast one captured image representative of features in an environment ofthe vehicle. For example, step 3210 may include receiving an image, suchas captured image 2910, which may have been captured by image capturedevice 2720 of vehicle 2700. The captured image may therefore includerepresentations of features within the environment of vehicle 2700, suchas lane markings 2912 and 2914. In some embodiments, the vehicle may bean autonomous vehicle, as described above.

At step 3220, process 3200 may include generating a warped image basedon the received at least one captured image. For example, warped image2920 may be generated based on captured image 2920 using the imagewarping techniques described above. The warped image may simulate a viewof one or more of the features in the environment of the vehicle from asimulated viewpoint elevated relative to an actual position of thecamera. For example, if the captured image is captured using imagecapture device 2720, the warped image may simulate viewpoint 2730 withsimulated optical axis 2732. The degree and type of warping that isapplied to the original image may depend on the height of the simulatedviewpoint and/or the angle of the simulated optical axis.

At step 3230, process 3200 may include identifying at least one roadfeature represented in the warped image. The at least one road featuremay correspond to any of the example road features described throughoutthe present disclosure. In some embodiments, for example, the identifiedat least one road feature may include a lane mark, such as lane mark2912 and/or 2914. For example, the at least one road feature may includeone or more lane marks indicating a lane split or a lane merge. In someembodiments, the identified at least one road feature may include aroundabout. In other embodiments, the identified at least one roadfeature may include a curve in the road. The representation of the atleast one road feature in the warped image may be transformed in one ormore respects relative to a representation of the at least one roadfeature in the at least one captured image. For example, the at leastone road feature in the warped image may be translated, scaled, and/orrotated in the warped image as compared to the captured image.

At step 3240, process 3200 may include determining a position of one ormore points associated with the identified at least one road feature.The determined position of the one or more points associated with theidentified at least one road feature may include the determinedaggregated leading edge point position and the determined aggregatedtrailing edge point position, as discussed above. Accordingly, step 3240may include one or more substeps consistent with the techniquesdiscussed above in reference to FIGS. 31A-31C. For example, determiningthe position of one or more points associated with the identified atleast one road feature may include, for each pixel of the warped imagethat overlaps a portion of the at least one road feature, predicting afirst point location associated with a leading edge of the at least onefeature and a second point location associated with a trailing edge ofthe at least one feature. In some embodiments, the first point locationand second point location may be determined using a machine learningmodel, as described above. Accordingly, multiple predicted first pointlocations and predicted second point locations may be determined. Step3240 may further include clustering the predicted first point locationsand clustering the predicted second point locations. For example,clusters 3142 and 3144 may be determined, as discussed above. Step 3240may further include determining an aggregated leading edge pointposition based on the clustered first point locations and determining anaggregated trailing edge point position based on the clustered secondpoint locations. Because they are based on an aggregation of multiplepoints, the determined aggregated leading edge point position and thedetermined aggregated trailing edge point position have sub-pixelaccuracy relative to a resolution of the warped image.

At step 3250, process 3200 may include forwarding the determinedposition to a server for use in updating a road navigation model. Forexample, processor 2710 may forward the determined position to server1230, which may use the determined position to generate a sparse map orother road navigation model. In some embodiments, forwarding thedetermined position may include transmitting the determined position toserver 1230 using a wireless transmitter device, such as transceiver172, as described above.

It should be noted that various steps of process 3000 and/or 3200 may bemodified or omitted. Additionally, or alternatively, the sequence ofsteps of process 3000 and/or 3200 may be modified. For example, inembodiments where process 3200 is performed by a server, such as server1230, step 3250 may be omitted and the determined position may be usedin updating the road navigation model as described throughout thepresent disclosure.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A navigation system for a vehicle, the systemcomprising: at least one processor comprising circuitry and a memory,wherein the memory includes instructions that when executed by thecircuitry cause the at least one processing device to: receive at leastone captured image representative of features in an environment of thevehicle, the at least one captured image being captured by a camera ofthe host vehicle; generate a warped image based on the received at leastone captured image, wherein the warped image simulates a view of one ormore of the features in the environment of the vehicle from a simulatedviewpoint elevated relative to an actual position of the camera, thewarped image being generated by transforming pixels corresponding to aground plane in the environment of the vehicle to a warped ground plane,the warped ground plane being at an angle relative to the ground plane,wherein generating the warped image includes adding at least oneadditional pixel to the warped image such that the representation of theat least one road feature in the warped image includes more image pixelsthan the representation of the at least one road feature in the at leastone captured image; identify at least one road feature represented inthe warped image, wherein the representation of the at least one roadfeature in the warped image is transformed in one or more respectsrelative to a representation of the at least one road feature in the atleast one captured image; determine a navigational action for thevehicle based on the identified at least one road feature represented inthe warped image; and cause at least one actuator system of the vehicleto implement the determined navigational action.
 2. The navigationsystem of claim 1, wherein the vehicle is an autonomous vehicle.
 3. Thenavigation system of claim 1, wherein the simulated viewpoint iselevated by at least ten meters relative to the actual position of thecamera.
 4. The navigation system of claim 1, wherein the simulatedviewpoint is elevated by between ten meters and twenty meters relativeto the actual position of the camera.
 5. The navigation system of claim1, wherein the warped image further simulates a view of the one or morefeatures in the environment of the vehicle based on a simulated cameraoptical axis angled downward relative to an actual optical axis of thecamera.
 6. The navigation system of claim 5, wherein the simulatedcamera optical axis is downwardly angled relative to the actual opticalaxis of the camera by an angle of between thirty-five degrees andfifty-five degrees.
 7. The navigation system of claim 1, wherein theidentified at least one road feature includes a lane mark.
 8. Thenavigation system of claim 1, wherein the identified at least one roadfeature includes one or more lane marks indicating a lane split.
 9. Thenavigation system of claim 1, wherein the identified at least one roadfeature includes a roundabout.
 10. The navigation system of claim 1,wherein the identified at least one road feature includes a curve in theroad.
 11. The navigation system of claim 1, wherein the determinednavigational action includes at least one of braking or a change inheading direction.
 12. The navigation system of claim 1, wherein theground plane is associated with a road surface.
 13. The navigationsystem of claim 1, wherein the identified road feature is located atleast sixty meters ahead of the actual camera position, and the camerahas a resolution of no more than three hundred pixels per inch.
 14. Amethod for navigating a vehicle, the method comprising: receiving atleast one captured image representative of features in an environment ofthe vehicle, the at least one captured image being captured by a cameraof the vehicle; generating a warped image based on the received at leastone captured image, wherein the warped image simulates a view of one ormore of the features in the environment of the vehicle from a simulatedviewpoint elevated relative to an actual position of the camera, thewarped image being generated by transforming pixels corresponding to aground plane in the environment of the vehicle to a warped ground plane,the warped ground plane being at an angle relative to the ground plane,wherein generating the warped image includes adding at least oneadditional pixel to the warped image such that the representation of theat least one road feature in the warped image includes more image pixelsthan the representation of the at least one road feature in the at leastone captured image; identifying at least one road feature represented inthe warped image, wherein the representation of the at least one roadfeature in the warped image is transformed in one or more respectsrelative to a representation of the at least one road feature in the atleast one captured image; determining a navigational action for thevehicle based on the identified at least one road feature represented inthe warped image; and causing at least one actuator system of thevehicle to implement the determined navigational action.
 15. The methodof claim 14, wherein the simulated viewpoint is elevated by at least tenmeters relative to the actual position of the camera.
 16. The method ofclaim 14, wherein the simulated viewpoint is elevated by between tenmeters and twenty meters relative to the actual position of the camera.17. The method of claim 14, wherein the warped image further simulates aview of the one or more features in the environment of the vehicle basedon a simulated camera optical axis angled downward relative to an actualoptical axis of the camera.
 18. The method of claim 17, wherein thesimulated camera optical axis is downwardly angled relative to theactual optical axis of the camera by an angle of between thirty-fivedegrees and fifty-five degrees.
 19. A system for mapping a road featurefor use in vehicle navigation, the system comprising: at least oneprocessor comprising circuitry and a memory, wherein the memory includesinstructions that when executed by the circuitry cause the at least oneprocessing device to: receive at least one captured image representativeof features in an environment of the vehicle, the at least one capturedimage being captured by a camera of the vehicle; generate a warped imagebased on the received at least one captured image, wherein the warpedimage simulates a view of one or more of the features in the environmentof the vehicle from a simulated viewpoint elevated relative to an actualposition of the camera, the warped image being generated by transformingpixels corresponding to a ground plane in the environment of the vehicleto a warped ground plane, the warped ground plane being at an anglerelative to the ground plane, wherein generating the warped imageincludes adding at least one additional pixel to the warped image suchthat the representation of the at least one road feature in the warpedimage includes more image pixels than the representation of the at leastone road feature in the at least one captured image; identify at leastone road feature represented in the warped image, wherein therepresentation of the at least one road feature in the warped image istransformed in one or more respects relative to a representation of theat least one road feature in the at least one captured image; determinea position of one or more points associated with the identified at leastone road feature; and forward the determined position to a server foruse in updating a road navigation model.
 20. The navigation system ofclaim 19, wherein the determination of the position of one or morepoints associated with the identified at least one road featureincludes: for each pixel of the warped image that overlaps a portion ofthe at least one road feature, predicting a first point locationassociated with a leading edge of the at least one feature and a secondpoint location associated with a trailing edge of the at least onefeature; clustering the predicted first point locations; clustering thepredicted second point locations; determining an aggregated leading edgepoint position based on the clustered first point locations; determiningan aggregated trailing edge point position based on the clustered secondpoint locations; and wherein the determined position of the one or morepoints associated with the identified at least one road feature includesthe determined aggregated leading edge point position and the determinedaggregated trailing edge point position.
 21. The navigation system ofclaim 20, wherein the determined aggregated leading edge point positionand the determined aggregated trailing edge point position havesub-pixel accuracy relative to a resolution of the warped image.
 22. Thenavigation system of claim 19, wherein the at least one road feature isa lane mark.
 23. The navigation system of claim 19, wherein the at leastone road feature is a lane mark associated with a lane merge.
 24. Thenavigation system of claim 19, wherein the at least one road feature isa lane mark associated with a lane split.
 25. A method for mapping aroad feature for use in vehicle navigation, the method comprising:receiving at least one captured image representative of features in anenvironment of the vehicle, the at least one captured image beingcaptured by a camera of the vehicle; generating a warped image based onthe received at least one captured image, wherein the warped imagesimulates a view of one or more of the features in the environment ofthe vehicle from a simulated viewpoint elevated relative to an actualposition of the camera, the warped image being generated by transformingpixels corresponding to a ground plane in the environment of the vehicleto a warped ground plane, the warped ground plane being at an anglerelative to the ground plane, wherein generating the warped imageincludes adding at least one additional pixel to the warped image suchthat the representation of the at least one road feature in the warpedimage includes more image pixels than the representation of the at leastone road feature in the at least one captured image; identifying atleast one road feature represented in the warped image, wherein therepresentation of the at least one road feature in the warped image istransformed in one or more respects relative to a representation of theat least one road feature in the at least one captured image;determining a position of one or more points associated with theidentified at least one road feature; and forwarding the determinedposition to a server for use in updating a road navigation model. 26.The method of claim 25, wherein determining the position of one or morepoints associated with the identified at least one road featureincludes: for each pixel of the warped image that overlaps a portion ofthe at least one road feature, predicting a first point locationassociated with a leading edge of the at least one feature and a secondpoint location associated with a trailing edge of the at least onefeature; clustering the predicted first point locations; clustering thepredicted second point locations; determining an aggregated leading edgepoint position based on the clustered first point locations; determiningan aggregated trailing edge point position based on the clustered secondpoint locations; and wherein the determined position of the one or morepoints associated with the identified at least one road feature includesthe determined aggregated leading edge point position and the determinedaggregated trailing edge point position.
 27. The method of claim 26,wherein the determined aggregated leading edge point position and thedetermined aggregated trailing edge point position have sub-pixelaccuracy relative to a resolution of the warped image.
 28. The method ofclaim 25, wherein the at least one road feature is a lane mark.
 29. Anon-transitory computer-readable medium storing instructions that, whenexecuted by at least one processor, are configured to cause at least oneprocessor to perform a method for navigating a vehicle, the methodcomprising: receiving at least one captured image representative offeatures in an environment of the vehicle, the at least one capturedimage being captured by a camera of the vehicle; generating a warpedimage based on the received at least one captured image, wherein thewarped image simulates a view of one or more of the features in theenvironment of the vehicle from a simulated viewpoint elevated relativeto an actual position of the camera, the warped image being generated bytransforming pixels corresponding to a ground plane in the environmentof the vehicle to a warped ground plane, the warped ground plane beingat an angle relative to the ground plane, wherein generating the warpedimage includes adding at least one additional pixel to the warped imagesuch that the representation of the at least one road feature in thewarped image includes more image pixels than the representation of theat least one road feature in the at least one captured image;identifying at least one road feature represented in the warped image,wherein the representation of the at least one road feature in thewarped image is transformed in one or more respects relative to arepresentation of the at least one road feature in the at least onecaptured image; determining a navigational action for the vehicle basedon the identified at least one road feature represented in the warpedimage; and causing at least one actuator system of the vehicle toimplement the determined navigational action.
 30. A non-transitorycomputer-readable medium storing instructions that, when executed by atleast one processor, are configured to cause at least one processor toperform a method for mapping a road feature for use in vehiclenavigation, the method comprising: receiving at least one captured imagerepresentative of features in an environment of a vehicle, the at leastone captured image being captured by a camera of the vehicle; generatinga warped image based on the received at least one captured image,wherein the warped image simulates a view of one or more of the featuresin the environment of the vehicle from a simulated viewpoint elevatedrelative to an actual position of the camera, the warped image beinggenerated by transforming pixels corresponding to a ground plane in theenvironment of the vehicle to a warped ground plane, the warped groundplane being at an angle relative to the ground plane, wherein generatingthe warped image includes adding at least one additional pixel to thewarped image such that the representation of the at least one roadfeature in the warped image includes more image pixels than therepresentation of the at least one road feature in the at least onecaptured image; identifying at least one road feature represented in thewarped image, wherein the representation of the at least one roadfeature in the warped image is transformed in one or more respectsrelative to a representation of the at least one road feature in the atleast one captured image; determining a position of one or more pointsassociated with the identified at least one road feature; and forwardingthe determined position to a server for use in updating a roadnavigation model.